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dissertation entitled

ECONOMIC SANCTIONS, DOMESTIC POLITICAL
INSTITUTIONS, AND UNITED STATES DRUG POLICY: THE
USE AND EFFECTIVENESS OF ECONOMIC COERCION

Ph.D.

 

presented by

David James Lektzian

has been accepted towards fulfillment
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ECONOMIC SANCTIONS, DOMESTIC POLITICAL INSTITUTIONS, AND UNITED
STATES DRUG POLICY: THE USE AND EFFECTIVENESS OF ECONOMIC
COERCION
By

David James Lektzian

A DISSERTATION
Submitted to Michigan State University
In partial fulﬁllment of the requirements for the degree of

DOCTOR OF PHILOSOPHY
Department of Political Science

2003

A;

5U:

ABSTRACT
ECONOMIC SANCTIONS AND DOMESTIC POLITICAL INSTITUTIONS: AN
EMPIRICAL ANALYSIS WITH A STUDY OF UNITED STATES SANCTIONS
AGAINST COUNTRIES SUPPORTING INTERNATIONAL NARCOTICS
TRAFFICKING
By

David James Lektzian

In this dissertation I use a general framework regarding the importance of
domestic politics in international relations to address two important topics in the study of
economic coercion; the initiation and the success of economic sanctions. The ﬁrst part of
the dissertation focuses on the initiation of sanctions. In this section, I perform an
empirical analysis including all sanctions cases from 1950-1989. The ﬁndings Show that
domestic political institutions inﬂuence the use of economic sanctions and supports the
idea of an “economic peace” among democratic states, at least regarding the use of
economic sanctions. While democratic states are more likely to use economic sanctions
than autocratic states, they are less likely to use them against another democracy.

The second part of the dissertation focuses on the success of economic sanctions.
A game theoretic model is developed and used to derive several hypotheses regarding the
success of sanctions at the threat and initiation stage. These hypotheses are tested using a
strategic probit model (STRAT) and data gathered on the US sanctions against countries
that do not cooperate in the ﬁght against the trafﬁcking of illicit narcotics. The ﬁndings
show that threats of sanctions have tended to be more successful than their
implementation, leading to a bias against ﬁndings of success in studies that only analyze

implemented sanctions. This helps answer one of the most puzzling questions in the

study of economic sanctions; why do states continue to use sanctions when they are so
likely to be unsuccessful? The simple answer proposed by this study is that states
continue to use sanctions because they expect to be successful. However, because most
of the success of sanctions takes place at the threat stage, traditional estimates of success
based on implemented sanctions are likely to be biased downward. A second important
ﬁnding is that when the potential target of sanctions is a democracy, both the sender and
the target are more likely to back down at the threat stage. This helps account for the
empirical ﬁnding in the ﬁrst part of the dissertation that democracies are less frequently

the target of sanctions.

Dedicated to the memory of my father, Sam Lektzian

iv

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0105

ACKNOWLODGEMENTS

The research presented in this dissertation represents my attempt to bridge theories
of economic coercion and military conﬂict. Most people never have the opportunity in
life to pursue, let alone complete, such a self-indulgent task. I warmly acknowledge
those who helped make it possible for me to step outside of ‘the real world’ for a period
of years and exercise my mind in this manner.

I have been fortunate to have two people in my life who made the completion of
this dissertation possible. The ﬁrst person came to me through the right of birth; the
other came about through the luck of placement. Of course there are many others who
played important roles in the completion of this dissertation, but the two who stand out
most prominently are my mother, Helen Lektzian, and my advisor, Scott Gates.

Scott Gates has and continues to be more than just an advisor; he is a friend and a
mentor. Without Scott’s guidance I would have still written a dissertation, but the end
product would not have been nearly as good and the task could not have been as
enjoyable. Most importantly, my mother has always provided unquestioned support
throughout my life and throughout the writing of this dissertation. Without her help I
would not have even begun the task, let alone ﬁnish it! My deepest thanks and gratitude
go out to both of you!

I believe every happy and successful graduate student needs somebody in his or her
cohort group to interact with. For me the perfect colleague and friend during my
graduate school years was Mark Souva. Likewise, we all need somebody to keep us from
getting too serious and distract us from work every now and then. The Reverend Mark

LaVelle always ﬁlled this role most enthusiastically! I was also blessed by having my

brother Michael, sister Joan, and my niece and nephew, Zack and Katy, live within 2
miles of my house during most of my graduate years.

I also beneﬁted greatly from those who provided support for my graduate studies
by giving me the opportunity to work in a challenging yet ﬂexible work environment.
Karen Klomparens provided the perfect work environment for graduate students. She
made sure that my work for The Graduate School was never a burden and that I always
had adequate facilities to complete my work for The Graduate School as well as my
dissertation. Likewise, having the opportunity to learn about the discipline from Ada
F inifter, as an intern for the APSR, has been invaluable.

It would be impossible to mention the contributions of all the other people who
helped along the way. My entire dissertation committee was most helpful and supportive
and helped make the end product stronger. In particular Bill Reed helped broaden my
understanding of the ﬁelds of International Relations and Quantitative Methods. Among
other faculty members inﬂuential in the writing of my dissertation, Jim Granato taught
methods in a way that was enjoyable and accessible; Brian Silver made sure I always had
flmding opportunities available; and Gretchen Hower provided a great foundation in how
to think critically. Fellow graduate students who helped develop my thoughts as well as
providing friendship were Brandon Prins, Chris Butler, Chris Sprecher, Sara Mitchel,
Wonjae Hwang, Sara Benesch, Gina Lambright, Lu-huei Chen, Matt Kleiman, BJ

Dobski, Mark Hurwitz, Misa Nishikowa, and Wendy Martinik.

vi

 

 

TABLE OF CONTENTS

 

 

LIST OF TABLES .................................................................................. VI
LIST OF FIGURES ..............................................................................
CHAPTER 1
Introduction — Why Study Sanctions ........................................................... 1
The Puzzling Use Of Sanctions ................................................................. 5
Studying Sanctions As A Type Of Conﬂict .................................................. 10
Plan Of The Dissertation ........................................................................ 14
CHAPTER 2
Deﬁning Sanctions And The Determinants Of Success .................................... 19
The Use Of Sanctions (Type, Scope, Domain) .............................................. 20
The Type Of Inﬂuence Attempted ............................................................. 20
Deﬁnition Of Sanctions — What Is A Sanction? ............................................ 20
Threats Of Sanctions ............................................................................. 30
Scope Of The Inﬂuence Attempt (Sanctions Success) ..................................... 37
The Determinants Of Successful Sanctions .................................................. 43
Economic Cost Of Sanctions ................................................................... 44
Value Of The Policy At Dispute ............................................................... 46
Domestic Political Factors ...................................................................... 48
Stability Of The Target Government ......................................................... 51
Value Of The Existing Relationship Between Target And Sender ....................... 53
Domain Of Inﬂuence Attempt (Sanctions Theory) ....................................... 55
Domestic Institutions And Economic Sanctions: An Institutional Explanation
Of How Sanctions Work ....................................................................... 56
A Public Choice Example ...................................................................... 62
CHAPTER 3
The Initiation Of Sanctions .................................................................... 65
Monadic Analysis ............................................................................... 67
Dyadic Analysis ................................................................................. 73
Research Design ................................................................................. 79
Results ............................................................................................ 90
Conclusions Regarding The Initiation Of Sanctions ....................................... 93
CHAPTER 4:
Threats, Success, And United States Drug Sanctions Policy .............................. 95
Threats Of Sanctions And Domestic Institutions .......................................... 101
The Observation Of Threats Of Sanctions: United States Policy
Toward International Narcotics Trafﬁcking ............................................... 106
Developing A Formal Theoretical Model Of Threats Of Sanctions .................... 112
vii

Restrictions Over Preferences ................................................................ 118

Solving The Game With Complete Information ........................................... 121
Solving The Game With Incomplete Information ......................................... 122
Implications Of The Formal Model ......................................................... 130
Summary Of Testable Hypotheses Developed In This Chapter ......................... 146
CHAPTER 5
Threats Of Sanctions: Converting The Game Theoretic Model To An
Empirical Model ................................................................................... 147
Research Design: Operationalizing Concepts In The Theoretical Model .................. 162
Description Of Data ............................................................................... 167
Dependent Variable: Outcome .................................................................. 168
Independent Variables ............................................................................ 173
Building The Strategic Probit Model ............................................................ 173
Results ............................................................................................... 186
Results Of The Strategic Probit Model .......................................................... 190
Performance Of The Model ...................................................................... 190
Interpreting The Results Of The Strategic Probit Model: .................................... 191
The Importance Of Regime Type ................................................................ 193
Value Of The Policy At Dispute ................................................................ 195
Drugs In Democracies ............................................................................ 198
The Cost Of The Sanctions ....................................................................... 198
Regional Factors ................................................................................... 202
Stability Of The Target .......................................................................... . .203
Summary Of Support For Hypotheses .......................................................... 205
APPENDIX A: DEVELOPMENT OF THE DEPENDENT VARIABLE ................ 217

APPENDIX B: COUNTRIES AFFECTED BY UNITED STATES

DRUG SANCTIONS POLICY SINCE 1987 .................................................. 218
APPENDIX C: JUSTIFICATION FOR CODING OF SUCCESS CASES ............... 219
APPENDIX D: INFORMATION ON SEIZURES

AS A MEASURE OF DRUG TRAFFIC ....................................................... 231
APPENDIX E: US DRUG SANCTIONS: COUNTRIES THREATENED

AND COUNTRIES SANCTIONED ............................................................ 235

viii

 

 

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LIST OF TABLES
TABLE 2.1: SUCCESS AND POTENTIAL COST OF SANCTIONS ...................... 44
TABLE 3.1: CROSS CUTTING CLEAVAGES ACTIVATED
DURING TRADE SAN CTIONS ................................................................. 70
TABLE 3.2: INITIATORS OF SANCTIONS BY REGIME TYPE, 1950-1990 .......... 80

TABLE 3.3: DETERMINANTS OF STATE INITIATION OF

SANCTIONS, 1950-1990 .......................................................................... 84
TABLE 3.4: TYPE OF SANCTIONS BY REGIME TYPE .................................. 85
TABLE 3.5: GOAL OF SANCTIONS BY REGIME TYPE ................................. 86
TABLE 3.6: SANCTIONS BY DYADIC REGIME TYPE .................................. 87

TABLE 3.7: DETERMINANTS OF THE ONSET OF
DYADIC SANCTIONS, 1950-1990 ............................................................. 90
TABLE 3.8: LENGTH OF SANCTIONS BY DYAD TYPE ................................ 93

TABLE 5.1: DESCRIPTIVE STATISTICS FOR VARIABLES

APPEARING IN EMPIRICAL ANALYSIS .................................................. 173
TABLE 5.2: TARGET COMPLIANCE WITH SANCTIONS THREAT ................ 185
TABLE 5.3: DOES SENDER CARRY OUT THREAT ..................................... 187
TABLE 5.4: CASES CORRECTLY PREDICTED .......................................... 190

TABLE 5.5: STRATEGIC PROBIT ESTIMATES:

1-2-1-2 MODEL WITH PRIVATE INFORMATION ....................................... 192
TABLE 5.6: CHANGE IN PREDICTED PROBABILITY FOR

SOUTH/CENTRAL AMERICAN DUMMY VARIABLE ................................. 215

TABLE 5.7: CHANGE IN PREDICTED PROBABILITY FOR

ix

 

INTERNAL CONFLICT DUMMY VARIABLE ............................................. 216

LIST OF FIGURES

FIGURE 1.1: FLOW DIAGRAM OF DISSERTATION ORGANIZATION .............. 15
FIGURE 4.1: SELECTION DIAGRAM ......................................................... 97
FIGURE 4.2: BASIC SELECTION MODEL OF SANCTIONS ............................. 97

FIGURE 4.3: THREATS OF SANCTIONS WITH COMPLETE

INFORMATION ................................................................................... 100
FIGURE 4.4: US DRUG SANCTIONS PROCESS .......................................... 109
FIGURE 4.5: SANCTIONS GAME WITH INCOMPLETE INFORMATION .......... 113

FIGURE 4.6: EFFECT OF CHANGING VALUE OF POLICY

ON DECISION TO THREATEN SANCTIONS ............................................. 134
FIGURE 4.7: EFFECT OF CHANGING THE EXPECTED

COST OF SANCTIONS ON THE DECISION TO THREATEN SANCTIONS. . . . .....136
FIGURE 4.8: EFFECT OF CHANGING THE VALUE OF THE RELATIONSHIP

ON THE DECISION TO THREATEN SAN CTIONS ....................................... 138
FIGURE 5.1: THREAT AND IMPLEMENTATION MODEL OF SANCTIONS ...... 149
FIGURE 5.2: GENERAL STRUCTURE OF SANCTIONS GAME ...................... 150
FIGURE 5.3: SANCTIONS THREAT MODEL WITH UNCERTAINTY
CONCERNING UTILITIES ..................................................................... 153
FIGURE 5.4: SPECIFICATION OF THE SENDER AND TARGET’S

UTILITIES IN TERMS OF REGRESSORS ................................................... 160
FIGURE 5.5: DRUG SANCTIONS MODEL ................................................. 164
FIGURE 5.6: EFFECT OF TARGET DRUG PROBLEM ON THE

PROBABILITY SENDER THREATENS SANCTIONS .................................... 207

xi

 

 

FIGURE 5.7: EFFECT OF TARGET DRUG PROBLEM ON THE

PROBABILITY SENDER BACKS DOWN AFTER THREATENIN G ................... 208

FIGURE 5.8: EFFECT OF SENDER TARGET TRADE LEVELS ON

PROBABILITY SENDER THREATENS ..................................................... 209

FIGURE 5.9: EFFECT OF TARGET SENDER TRADE ON PROBABILITY

SENDER BACKS DOWN AFTER THREATENING ....................................... 210

 

‘ FIGURE 5.10: EFFECT OF TARGET TRADE AS % GNI ON

PROBABILITY TARGET BACKS DOWN AFTER THREATENED ................... 211
FIGURE 5.11: EFFECT OF TARGET AID AS % GNI ON

PROBABILITY SANCTIONS ARE SUCCESSFUL ........................................ 212
FIGURE 5.12: EFFECT OF TARGET TRADE AS % OF GDP ON

PROBABILITY TARGET BACKS DOWN AFTER THREATENED ................... 213

 

FIGURE 5.13: EFFECT OF TARGET TRADE AS % OF GDP ON

PROBABILITY SANCTIONS ARE SUCCESSFUL ........................................ 214

xii

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Chapter 1
Introduction - Why Study Sanctions

Economic sanctions play an essential, yet under appreciated, role in the
interactions between states. As a policy option that ﬁts between the two poles of
military force and doing nothing sanctions have been employed by governments
all around the world with increased frequency and great diversity in the types of
issues to which they are applied. Since 1990 alone, economic sanctions were
used by the United States, Greece, Russia, the United Nations and the European
Union, China, Germany, Belgium, France, Saudi Arabia, England, the
Netherlands, Spain, Japan, the OAU and ECOWAS, Mercosur, and Turkey to take
on goals such as discouraging the proliferation of nuclear weapons and ballistic
missile technology, promoting human rights, combating terrorism, curtailing drug
trafﬁcking, gaining mineral rights, ending armed conﬂicts, and assisting in the
destabilization of govemmentsl.

The United States has been a particularly frequent user of economic
sanctions leading Richard Haass, the Director of Foreign Policy Studies at
Brookings, to describe sanctions as “the policy tool of choice for the United States
in the post-Cold War world” (Haass, 1998:1). Similarly, Zachary Selden, the
Director of the Business Executives for National Security (BENS) has called
sanctions “Virtually the default option in American foreign policy” (Selden, 1999:

ix).

 

 

 

end of
the [p

33mm

That policy makers have turned to sanctions with an increasing degree of
frequency over the last decade is undisputed. Elliot and Hutbauer (1998) summarizing
data collected at the Institute for International Economics show the imposition of at least
50 new cases of sanctions in the 19905 alone after counting 115 cases in the entire period

from 1914 — 1989. Furthermore, a recent study by the Brookings institute (O’Sullivan,

 

2003) counts 66 new sanctions by the United States or UN during the 1990-2002 period.

There have been various reasons offered for the increased use of sanctions in the
199052. One is that the dissolution of the Soviet Union has left the United States
searching for a post cold war paradigm and this has lead to an increased demand to “do
something” about many different problems around the world, including ethnic conﬂict,
human-rights violations, drug trafﬁcking, terrorism, or nuclear proliferation

A second explanation for the increased use of sanctions in the 1990s is that the
end of the cold war has freed the UN to act now that the East-West gridlock created by
the United States-Soviet opposition has ended. As a result there have been 15 new UN
sanctions since the 19905 (O’Sullivan, 1999) compared to only two prior to 1990.

A third explanation is that the international economy has become more integrated,
producing a greater ability for states to use sanctions for coercive diplomacy. However,

economic interdependence is a double-edged sword for sanctions because while it

 

' See Hufbauer, and Oegg (2000) for a list of sanctions cases since 1990.

2 Aside ﬁ'om the explanations offered here van Bergeijk (1995) offers six additional factors to explain
“why sanctions are and will be increasingly used: the end of the Cold War; strategic trade policy
considerations; the greening of trade issues; the proliferation of weapons of mass destruction and the related
technology; scale economies in the implementation of sanctions; and the process of globalization” (van
Bergeijk, 1995: 445).

 

increases the potential vulnerability to the disruption of trade ﬂows, it also increases the
potential for evasion by targets of sanctions.

Whatever the underlying reasons for their increased usage, the paucity of
academic analysis of sanctions compared to studies of military conﬂict is
surprising and somewhat disturbing. David Baldwin in his treatise on economic
statecraft passionately argues for scholars to study options other than military

statecraﬁ. He says

1..

“Any generalization about the utility of military force as a
technique of statecraft made without reference to the utility of
alternative techniques is likely to be both intellectually misleading
and socially irresponsible. Given the plethora of studies of
military statecraft at a high level of generality, most of which make
no serious attempt to consider alternative techniques, scholars are
derelict in fulﬁllment of their social responsibilities if they fail to
provide studies of alternative techniques of statecraft at comparable
levels of generality. . .Since the books on military statecraft cannot
be unwritten, the only socially responsible alternative is to generate
studies of alternative policy techniques at comparable levels of
generality” (Baldwin 1985: 67)

In this dissertation, as Baldwin urges, economic statecraﬁ will be studied at a level
consistent with studies of military force, thus at least partly ﬁlling the void that
Baldwin laments in the above quote.

Aside from contributions to the academic study of economic sanctions, the
ﬁndings of the dissertation also have important implications in the realm of policy. With
the rise in their use and increased complexity of issues for which they are applied,
understanding the mechanisms that produce successful or failed sanctions has become
increasingly important. Sanctions policies that miss the mark by applying pressure in

ways that should be predictably ineffective, are likely to cause increased economic

 

hardship on poor segments of the target state’s population. The massive economic
damage inﬂicted on Iraq by UN sanctions between 1990 and 2002 without achieving
stated policy objectives has forced the world to take notice of the consequences of
misusing economic sanctions. If there was ever a doubt about the ability to punish a
country economically through sanctions, it has vanished in the wake of the Iraqi
sanctions. Consider that prior to the sanctions imposed on Iraq in 1990, one Iraqi dinar
was worth three-and-a-half US. dollars. By 1999 it took just under 2,000 Iraqi dinars to
make a dollar3. (Roberts, 1999) Clearly, sanctions should not be used lightly, nor should
they be used without an attempt to understand their repercussions.

When considering the use of economic sanctions, it is essential to understand
how, or if, economic hardship will be translated to the achievement of political
objectives. Perhaps the most important lesson to understand about economic sanctions is
that effective sanctions (in terms of economic damage) do not always equal successful
sanctions (in terms of policy achievement).

Woodrow Wilson demonstrated an appreciation for the power of sanctions to
punish when in a speech in 1919 he uttered these famous words,

“A nation that is boycotted is a nation that is in sight of surrender. Apply

this economic, peaceful, silent, deadly remedy and there will be no need

for force. It is a terrible remedy. It does not cost a life outside the nation

boycotted, but it brings a pressure upon the nation which, in my judgment,
no modern nation could resist” (Padover, 1942: 108).

 

3 While this is a comparatively objective measure of the consequences of the sanctions, many
researchers cite ﬁgm'es on the increased number of deaths of children under 5 in Iraq as a result of the
sanctions. These estimates range widely from about 237,000 to about 1.5 million children dead as a result

of sanctions.

 

 

What Wilson failed to understand (although it was made painfully clear in the
failed attempt by the League of Nations to use economic sanctions to coerce Italy
in 193 5) was that while sanctions may indeed be a terrible remedy in respect to
their ability to impose economic hardship, this hardship will not necessarily

translate into surrender by the target. Simply put, some governments will be less

 

responsive to economic coercion attempts than others regardless of the economic
damage imposed. This point was also made clear in the Iraqi sanctions, and it
demands that we strive for a better understanding of the workings of this “terrible
.remedy.” However, the ultimate irony for the statesperson may be that it is not
sanctions that are a terrible remedy; it is their improper use, which has terrible
consequences.
The Pauling Use of Sanctions

Contrary to the opinion of Woodrow Wilson, researchers have been highly
skeptical of the ability of sanctions to accomplish their policy goals and many have come
to the conclusion that sanctions are simply an ineﬁ‘ective policy tool. Johan Galtung in
one of the most frequently cited works on economic sanctions concludes “In this article
the conclusion about the probable effectiveness of economic sanctions is, generally,
negative” (Galtung, 1967; p. 409). Barber (1979) and Olson (1979) ﬁnd the same
conclusions in their reviews of the literature. “There is a strong consensus that sanctions
have not been successful in achieving their primary objectives” (Barber, 1979; p. 384).
“There is a consensus in the literature that economic sanctions are largely ineffective.”
(Olson, 1979; p. 473). In a more recent work, Cortright and Lopez come to the same

conclusion as evidenced by this statement “Yet the conventional belief, seemingly

F0.

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10

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supported by the scholarly research, is that sanctions do not wor ” (Cortright and Lopez,
2000; p.13).

All of these negative assessments of the effectiveness of sanctions as a policy tool
lead to what is probably the most frequently addressed puzzle by sanctions researchers;
why do states use economic sanctions when they have such a dismal record of success.
For example, Lindsay (1986) asks “Yet if trade sanctions do not work, why do states
continue to impose them” (Lindsay 1986: 153) In other words, if sanctions are so unlikely
to succeed, why are they turned to so frequently to address important security concerns?
And what is particularly puzzling is why the senders of sanctions are so frequently
powerful countries that have many other foreign policy tools at their disposal, but still
choose sanctions. In other words, why, out of all the options available, would policy
makers knowingly choose one with such a low success rate?

In this dissertation, I propose a unique answer to the question of why sanctions are
used so much when they are so unlikely to be successful. The direct and intuitive answer
is that sanctions are simply not an unsuccessful policy, or they would not be used as often
as they are. The reason that traditional studies of sanctions have been so far off the mark
on this issue is that traditional empirical estimates of sanctions success are likely to be
biased downward because successful threats of sanctions are ignored. I develop this line
of reasoning through a deductive model accounting for threats of sanctions that explains
why states may rationally continue to use sanctions as a foreign policy tool even in the
face of low success rates for those sanctions that are implemented.

This deductive model shows that most of the success of economic sanctions

should be seen at the threat stage and not after they are implemented. Once, the two sides

 

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have actually engaged in implemented sanctions, the probability of success is shown to be
much lower. Therefore, analysts who base their assessment of the usefulness of sanctions
on their observed empirical success rate are only seeing part of the picture; and the most
dismal part at that. Viewing the policy in its entirety (including the success and failure of
threats) shows that it is entirely reasonable for policy makers to use sanctions with the
expectation of reaching success. Furthermore, in order for senders of sanctions to

. increase their credibility and prevent targets from anticipating that the sender will back
down, it may be perfectly rational for senders to leave ineffective sanctions policies in
place for long periods of time to Signal their resolve. In other words, it may be quite
necessary to have failed cases of sanction in order to demonstrate resolve on the part of
the sender and lead to successful future threats of sanctions.

While the previous discussion focuses on the formulation of a deductive model
and its uses in the derivation of formal hypotheses. I also perform an empirical test of
these hypotheses. Gates and Humes ( 1997) describe the method used here for analysis in
the following way

“Using the deductive approach, a game theoretic model is developed and

formalized form a more general theory to model a social interaction.

Assumptions (initial conditions) and equilibria for the game are then

identiﬁed. From this analysis, a set of propositions are presented and

empirically evaluated. The empirical analysis essentially tests

propositions derived from the analysis and identiﬁcation of equilibria”

(Gates and Humes, 1997:15).

In order to test the hypotheses of the deductive model, a new database of sanctions
cases, which includes threats of sanctions, is created. This data includes all threats and

impositions of sanctions by the United States from 1986-2002 against countries

supporting the illegal trafﬁcking of narcotics. Important independent variable concepts

are
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are operationalized with data on the amount of drug production and trafﬁc for every
country from 1987-2000 gathered from United Nations sources and supplemented from
United States sources where UN data was missing“. Thus, a major innovation in this
research is that unlike other deductive studies that focus on cases of implemented
sanctions, this study is able to analyze the importance of the entire sanctions policy,
including threats.

The results of the empirical model also lend strong support to the importance of
regime type in sanctions. Of particular importance for this study is that the biggest
impact of target democracy level was found to be at the threat stage. After sanctions have
been threatened, they are signiﬁcantly less likely to occur between democracies. These
ﬁndings help reconcile the expectation common in the literature that sanctions are more
likely to be successful against democratic governments but at the same time are
empirically less likely to occur between democracies (as demonstrated empirically later in
this dissertation)5.

In one of the most important ﬁndings of the dissertation, the United States is
found to threaten other democracies with regularity, as they should do if they expect
success, but those cases involving democracies as targets are signiﬁcantly less likely to
reach the implementation stage because both the sender and the target are more likely to
back down. This shows the strong signaling qualities of sanctions when democracies are
involved. Both senders and targets are found to be more likely to back down when a

democracy signals its intentions by using or resisting sanctions.

 

‘ Of course, there was still some missing data. See chapter 5 for complete details on this data set.

may.

Without this ﬁnding there is an unresolved question for all studies of sanctions
success and regime type. That question is that if democracies are less able to resist
sanctions (as proposed by the sanctions and democracy literature), then why are they less
likely to be sanctioned? If potential senders of sanctions are behaving rationally, and they
have a higher utility for successful sanctions than failed sanctions, then democracies
should be sanctioned more frequently. However, democracies are actually less frequent
targets of sanctions and the ﬁndings of this dissertation help explain why.

There are actually two parts to the explanation as proposed here. First, as shown
in the initiation section of the dissertation, democracies are the most frequent users of
sanctions, but are less likely to use them against similar regimes. This results in fewer
sanctions against democracies because of ideological similarities. Second, sanctions
against democracies are less likely to occur because of the ability of democracies to send
more credible signals. Thus when a democracy resists sanctions, the potential sender is
more likely to back down without a sanction being recorded. Also when a democracy is
truly unable to resist the sanctions once they are imposed, they are again more credible
and are more likely to give in at the threat stage rather than blufﬁng and having to give in
once the sanctions are enacted. Irnportantly, in both instances sanctions are never
Observed in traditional studies of sanctions. This leads traditional studies of sanctions to
underestimate the frequency with which democracies are targeted by sanctions.

Therefore, we see that by framing the research question within a model that

includes the importance of domestic institutions and by considering the successful use of

 

5 Similarly, Drezner (1999) predicts fewer sanctions against allies even though he predicts states to be

more successful against allies than adversaries.

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threats of sanctions, state behavior is found to be not so puzzling. States use sanctions
because they expect to be, and very often are, successful. However, because much of this
success is occurring at the threat stage it is unobserved by typical empirical analysts.
Therefore, much of what is cast by others as puzzling is shown here to be quite expected.
Studying Sanctions as a Type of Conﬂict

In addition to the explanations provided in the ﬁrst section of this chapter
for the increased use of sanctions in the 19908, Baldwin provides another
explanation for the general attractiveness of sanctions relative to other policy
options. Baldwin describes sanctions as ﬁlling an important space between using
military force and doing nothing. He says, “Economic sanctions lie somewhere in
between war and appeasement in terms of a continuum of toughness.” (Baldwin
1985:104) “[Sanctions] are stronger than diplomatic protests but weaker than
military attack” (Baldwin 1985: 104). Another analyst has adeptly noted that
“Sanctions straddle the line between the issue areas of national security and
international economics, giving rise to unique theoretical and policy puzzles”

(Martin, 1992; p.1).

Throughout the dissertation I build on the perspective that sanctions occupy this
position between security studies and international economies, which leads me to take a
vastly different approach to the study of sanctions. Previously scholars have approached
the study of sanctions from an economic perspective with an interest in the economic
implications of trade restrictions. However, if one sees sanctions as a type of conﬂict
short of war, they can be analyzed in much the same way as in studies of militarized

conﬂict. While the economic impact of sanctions is considered throughout this work, the

10

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primary focus is on Studying sanctions as a type of conﬂict. Scholars studying military
conﬂicts have already thoroughly explored the effect of domestic institutions on the
initiation, duration, severity and outcomes of wars, but such analysis has not typically
been performed on economic conﬂicts. Treating sanctions as conﬂicts provides an
opportunity to extend existing research on militarized disputes to the realm of economic
disputes, thus developing an exiting new area of research in International Relations.
While the conﬂict literature is rife with theoretical perspectives, in this
dissertation the body of work generally referred to, as the ‘Democratic Peace’ will
provide the theoretical framework for most of the analysis. One of the most robust
ﬁndings in all of International Relations is that democratic countries will not ﬁght wars
with each other. This ﬁnding has been hailed as “the closest thing to an empirical law in
world politics” (Levy 1988: 662). However, until very recently, this has not been a topic
of study for those interested in the use of economic force. Kim Richard Nossal, a leading
sanctions researcher, notes that scholars studying sanctions “. . .have tended to overlook
one important variable: the impact that regime type has on the success or failure of
international sanctions as an instrument of global governance” (Nossal 1999; p.127).
Nossal has identiﬁed a critical deﬁciency in the sanctions literature that pertains
not only to the success or failure of sanctions, but the onset, or initiation, as well. One of
the main theses developed in this research is that, much as in the democratic peace
literature, regime type inﬂuences the behavior of states in the use of economic sanctions.
However, since the costs associated with economic sanctions are less than those

associated with uses of military force the relationship will not be as strong.

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The 1956 Suez crisis provides an example of this relationship. When Great
Britain and France aided Israel in attacking Egypt without the approval of their ally, the
United States, three fully democratic countries squared off on opposite sides of a
potentially explosive dispute. The United States certainly wanted to exert pressure on its
democratic allies Great Britain and France to disengage their militaries from the Sinai and
honor an immediate ceaseﬁre. But when the UK and France used their UN Security
Council veto power to block the United States’ call for an unconditional cease-ﬁre, the
United States had to decide what level of force was most appropriate for achieving its
goal. In the end, the United States was willing to impose very costly economic sanctions
that are estimated to have cost Great Britain approximately $167 million (Hufbauer,
Schott, and Elliot 1985: p. 277), but a United States military strike against Great Britain
and France was never seriously considered. Hufbauer Schott and Elliot provide this
assessment of the situation.

From late October through early November, [the] UK’S oil reserves

are nearly depleted and [a] serious run on the pound threatens its

ﬁnancial position. [The] United States blocks attempts by [the]

UK to draw on its IMF reserves, refuses to provide ﬁnancial

assistance to support [the] pound unless [the] UK accepts UN

recommendations, [and] promises Eden [3] $1billion loan as soon

as British troops are withdrawn from [the] Suez. Totally beaten,

Eden capitulated at noon on 6 November. (Hufbauer, Schott, and

Elliot, 1985: p.278)

An important question for consideration is whether the behavior described above,
where one democracy is willing to impose costly economic sanctions against a
democratic ally, is typical of international relations or an exceptional case. Another area

for consideration pertains to the success of this inﬂuence attempt. Great Britain was one

of the most economically and militarily powerful countries in the world, yet it acquiesced

12

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to the demands of the United States after costly sanctions were imposed. An explanation
based on the importance of domestic institutions is provided in the dissertation for how
the United States was able to persuade Great Britain to change behavior through the use
of economic coercion.

By comparison, consider the response of the Soviet Union when the United States
tried this same tactic two decades later. After the Soviet Union’s invasion of Afghanistan
in December 1979, the United States again responded to military aggression by a major
world power with harsh economic sanctions, which included an embargo of United States
grain shipments (Hufbauer, Schott, and Elliot, 1985:655). These sanctions heightened
tensions between the countries and brought economic and political pressure on the
Soviets as well as imposing political and economic costs on the Carter regime from the
United States’ agricultural community. Once again, even if the United States was
unwilling to go to war over the issue, it was willing to impose costly sanctions to signal
its disapproval for the action and demonstrate its resolve.

Assuming that the costs were prohibitively high for confronting the Soviets
militarily in Afghanistan, the United States could have chosen to do nothing or to publicly
condemn the actions without the accompaniment of sanctions. However, as Selden
(1999) has pointed out, as a great power the United States pays a high price for inaction
when others perceive them to have the ability to act. Thus doing nothing becomes a very
costly response for great powers, which can send unintended signals to the rest of the
world. If the United States chose to do nothing in response to the Soviet’s invasion it
may have signaled disinterest on their part, a general weakness that precluded any

response, or tacit support for the actions. Issuing a public condemnation of the invasion

l3

without sanctions would have also signaled a lack of commitment on the part of the
United States. Paarlberg (1980) summarizes the difﬁcult situation facing the president
after the Soviet’s December invasion.

However slim its chances of success, the grain embargo must have

appeared, last January, as more attractive than the alternatives. The

alternative was to continue with plans. . .to sell in 1980 an all-time record

25 million tons of United States grain to the Soviet Union. . .Without an

embargo of some kind, the President would have found himself presiding

over the largest ‘Russian grain deal’ on record. . .To important allies

abroad, this would have been an inappropriate token of the

administration’s new policy toward the Soviet Union, so soon aﬁer

Afghanistan. . .Politically, the only thing worse than announcing a grain

embargo two weeks before the Iowa caucuses would have been not to

announce such an embargo.” (Paarlberg, 1980: 160)
In the end, even though the sanctions imposed high costs to both the Soviet Union and the
United States and may have actually been the best of the available options at the time,
they were eventually lifted without achieving any of their stated goals.
Plan of the Dissertation

I want to emphasize two points from the preceding examples, which pertain to the

two main themes of this dissertation. These two main themes can be seen as the two

branches in the ﬂow chart in Figure 1.1.

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The top branch in the ﬁgure Shows the theme of the initiation of sanctions, which
will be taken up in chapter 3, while the bottom branch shows that the theme of the
success of sanctions will be the subject of chapters 4 and 5. On the far left of the
diagram, Chapter 2 starts with a thorough review of existing theories regarding the use of
and success of sanctions and proceeds to develop a unique theoretical perspective
emphasizing the importance of domestic institutions. Thus, chapter 2 provides the
theoretical “glue” (if you will) that holds the dissertation together.

Referring back to the Soviet grain embargo and the Suez sanctions, we see ﬁrst
that pertaining to initiation, sanctions were used in a way that corresponds to Baldwin’s
characterization of them as a policy instrument lying between war and appeasement. In
both cases the United States was unwilling to go to war, but they were also unwilling to
give tacit approval by not responding. Thus in both cases, we see decidedly political
conﬂicts where economic coercion is used in hopes of achieving a change in a political
policy. Chapter 3 takes up the decision to impose sanctions by performing a detailed
empirical analysis of the factors affecting the decision to use sanctions.

An essential theoretical question proposed in this chapter is whether there is a
minimum threshold of hostility level to which theories of the democratic peace apply.
Or, in other words, is there an “economic peace” between democratic countries with
regard to uses of economic sanctions that mirrors the ﬁnding of the democratic peace
with regard to militarized disputes? Or, on the other hand, when the costs involved in the
conﬂict drop to the level involved in economic sanctions, will countries be quick to
respond with economic force regardless of ideological similarities (as was the case in the

United States sanctions against Great Britain)?

16

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The results of the statistical analysis in chapter 3 provide convincing evidence that
even though democracies are more likely to use economic sanctions than other nations,
they are signiﬁcantly less likely to use them against fellow democratic nations. However,
unlike in militarized conﬂicts, democracies are not altogether unwilling to use economic
force against other democracies. Democracies are the most frequent users of economic
sanctions, and, as evidenced in the Suez case, they will not completely exclude this lower
cost option as a tool of coercion against fellow democracies. But when democracies do
use sanctions against each other, domestic institutions present in democracies result in
speciﬁc types of sanctions being imposed. For example, democracies prefer to impose
ﬁnancial sanctions as they cause less harm to the general public. I also ﬁnd that the
greater incentive in a democracy to promote the public welfare leads democracies to
impose minor sanctions more often than autocracies.

The unambiguous conclusion of the analysis of chapter 3 is that domestic
institutions inﬂuence the use of and responses to economic force. Similar to the results of
the democratic peace regarding militarized force, countries with democratic institutions in
place are more likely to use economic sanctions, but are less likely to use them against
other democracies.

The second point from the two examples is that while the United States applied
costly sanctions over aggressive military behavior in both cases, they were successful
against Great Britain and not against the Soviet Union. Thus, a second theme of the
dissertation (taken up in chapters 4 and 5) is an exploration of the factors affecting the
successful use of economic sanctions. Chapter 4 is a theoretical chapter exploring the

role of threats in the use of economic sanctions with a special emphasis on the importance

17

 

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of domestic institutions. A formal, game theoretic, model is developed showing how
senders and targets of economic coercion can be expected to behave once economic
sanctions have been threatened. The theoretical foundation of the model is that domestic
political institutions can help explain why some targets of sanctions might comply at the
mere threat of sanctions while others might resist complying even under the heaviest of
sanctions. Chapter 5 subjects the ﬁndings of this model to empirical tests using data from
the United States’ sanctions policy against countries that are not cooperating in the ﬁght
against international trafﬁcking in illicit narcotics.

A ﬁnal point is that the Soviet Union and Great Britain had very different forms of
government at the time they were targeted by the United States with sanctions in the two
examples. As mentioned above, an important theme throughout the dissertation, which
ties together the initiation and success of sanctions, is the regime type of the governments
involved. Thus, before presenting the separate treatments of the initiation and success of
sanctions a general framework describing the importance of domestic institutions is
developed in the following chapter. The theory developed combines facets of both the
democratic peace literature and a public choice approach to the use of economic
sanctions. Before proceeding to the development of the theory, a general review of the
sanctions literature will be performed in order to establish a deﬁnitional language and to

evaluate previous arguments regarding domestic institutions and economic sanctions.

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Chapter 2
Defining Sanctions and the Determinants of Success

There are two main branches to this study, which are tied together by the
importance of domestic institutions in the target and sender of sanctions. The ﬁrst branch
involves the study of the factors affecting the initiation of sanctions and the second
addresses the factors leading to their success. The majority of the literature on sanctions
addresses the question of success in some manner, and most authors have painted a rather
pessimistic view regarding the potential for sanctions to achieve their stated foreign
policy goals. However, there is little consensus as to why that is the case, or how
important the achievement of stated goals is to the determination of the success of
sanctions. It is my contention that much of the reason for this lack of consensus
regarding the success of sanctions is due to vastly different deﬁnitions of what a sanction
is and when a sanction is successful.

This lack of clarity in terms is also partly to explain for the puzzle mentioned
earlier of why states continue to use sanctions when they are unlikely to be successful.
For instance, some analysts have included as economic sanctions, such punishments as
diplomatic restrictions, travel bans, sports bans, arms embargoes and other non-economic
punishments as well as trade and ﬁnancial restrictionsé. Deﬁning success has been just
as inconsistent with some attributing success only if stated goals are achieved and a direct
link can be established between the imposed economic restriction and the change in

behavior of the target (best exempliﬁed by Pape, 1997), while others have allowed for

 

6 For example both O’Sullivan (2003) and Doxey (1997) include UN imposed diplomatic sanctions

against Sudan in 1996 among their cases of “sanctions”.

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success to be achieved simply through the ability to punish a perceived wrongdoer (best
exempliﬁed by Nossal, 1989).

Thus, in the following sections I will discuss the differing perspectives found in
previous research on what sanctions are and how to achieve and evaluate their success.
The Use of Sanctions (Type, Scope, Domain)

Based on Baldwin’s typology of economic statecraft (Baldwin, 1985; p.32),
previous work on economic sanctions can be analyzed in terms of three areas. First, what
is the type of policy instrument used? Or, in other words, what is and is not included as
an economic sanction. Second, what is the scope of the inﬂuence attempt? Or, what is it
that sanctions are tryingto achieve, and how does one conclude that they have been
successﬁrl? Finally, what is the domain of the inﬂuence attempt, or who are sanctions
designed to affect in order to be successful? Each of these three areas (type, scope, and
domain) will be addressed in the following sections.

The Type of Influence Attempted
Definition of Sanctions -— What is a Sanction?

It is essential to establish a clear deﬁnition of what is and is not included as an
economic sanction. This is true not only for the obvious reason that it clariﬁes the
boundaries for the discussion, but it also helps clarify when sanctions have failed and
when they are successful. Unfortunately, the sanctions literature is anything but clear ‘
about what constitutes a sanction. Nossal (1989) laments this lack of clarity and points
out that it inhibits the general understanding of sanctions. “One of the impediments to
clear discussions about the useful purposes of sanctions is the use of the term “sanction”

itself. Deﬁnitions tend to be idiosyncratic, often sloppy, and frequently in violation of the

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minimum requirement that a word be deﬁned in a way that generally conforms to
common usage” (Nossal, 1989; p.304).

Lack of clarity in determining what a sanction is can lead to ambiguity about
factors that lead to sanctions usage as well as what constitutes a successful sanction. This
problem is particularly acute for analysts testing empirical models with secondary data
sources. It is essential that the deﬁnition for case selection is clear and that the cases are
. actually selected based on criteria matching the author’s criteria. It makes little sense to
deﬁne sanctions in the theoretical realm in one way and then perform tests on cases ﬁtting
a different deﬁnition. The most widely used source of data for empirical studies of
sanctions is Hufbauer, Schott, and Elliot (1985) who deﬁne sanctions as “. . .the deliberate
government-inspired withdrawal, or threat of withdrawal of ‘customary’ trade or ﬁnancial
relations” (Hufbauer, Schott, and Elliot, 1985: p.2). Hufbauer, Schott and Elliot go on to
say, “We deﬁne foreign policy goals to encompass changes actually and purportedly
sought by the sender state — the country imposing sanctions - in the political behavior of
the target state.” And, “We exclude from foreign policy goals the normal realm of
objectives sought in banking, commercial, and tax negotiations between sovereign states”
(Hufbauer, Schott, and Elliot, 1985: p.2). Thus empirical analysts using Hufbauer,
Schott, and Elliot’s data, whether they agree with this deﬁnition or not, are constrained to
report ﬁndings based on this deﬁnition or explain how the differences inﬂuence results.

Therefore, as Nossal (1989) notes, providing a clear deﬁnition of sanctions “is not
merely a semantic exercise designed to add yet another idiosyncratic deﬁnition to an ever-
expanding stock” (Nossal, 1989; p. 305). Providing a clear deﬁnition of the scope of

sanctions is an essential part of any sanctions research. Thus, in an effort to avoid

21

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ambiguity in deﬁnition, I will deﬁne sanctions as used in this study as uses of economic
means to coerce another State to do something that it would not have otherwise done in
the political realm7. An important element of this deﬁnition is that sanctions are viewed
as essentially political disputes linking economic means to political ends. As such, I view
sanctions as a clear escalation of hostility between sender and target that is above what is
seen in normal economic intercourse between nations.

This deﬁnition is developed, in part, from the deﬁnition of economic statecraft
provided above from Hufbauer, Schott and Elliot, and in part from Baldwin (1985).
While I point out important differences between Baldwin’s deﬁnition of sanctions and
that used here, Baldwin’s “Economic Statecraft” is one of the most inﬂuential works on
Economic Statecraft and it is used as a point of comparison for much of the discussion
that follows. Baldwin deﬁnes economic statecraft as inﬂuence attempts relying primarily
on resources that have a reasonable semblance of a market price in terms of money. The
essential difference between Baldwin’s deﬁnition of sanctions and the one used here is
that Baldwin’s deﬁnition includes all types of ends (beliefs, attitudes, opinions,
expectations, emotions, and/or propensities to act). The deﬁnition used here (like that
used by Hufbauer, Schott, and Elliot) departs from Baldwin, and is more traditional, in
that it differentiates between economic inﬂuence attempts that are essentially political in
their ends and those that are essentially economic in ends. Others who have made this
distinction include, for example, Bienen and Gilpin (1979) who exclude the imposition of

tariff barriers to retaliate against foreign trade barriers from their deﬁnition of economic

 

7 The terms economic sanction, economic coercion, and economic force are all used interchangeably

throughout the dissertation.

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sanctions on the grounds that the primary purpose of those actions is “economic rather
than political” (quoted in Baldwin p.47).

Lindsay (1986) also explicitly excludes economic means used to achieve
economic ends. Lindsay deﬁnes sanctions in the following way: “Trade sanctions can be
deﬁned as measures in which one country (the initiator) publicly suspends a major portion
of its trade with another country (the target) to attain political objectives” (Lindsay, 1986:
p. 154, emphasis in original). Lindsay goes on to say “. . .the pursuit of political objectives
distinguishes sanctions from curbs on trade designed to secure economic ends” (Lindsay,
1986: p.154).

Another important work on economic sanctions, Barber (1979), begins by clearly
deﬁning sanctions as not to include economic means for economic ends, “Economic
sanctions are economic measures directed to political objectives” (Barber, 1979, p.367).
Thus, following the lead of Bienen and Gilpin, Lindsay, Hufbauer, Schott, and Elliot, and
Barber, economic means used to achieve economic ends are excluded from the deﬁnition
of “economic sanctions” used here.

Baldwin (1985) also has one of the most detailed and extensive typologies of
sanctions. He breaks sanctions down into positive and negative sanctions, and then
further dissects each of those categories into trade and capital controls. He deﬁnes 11
types of negative trade sanctions, 7 types of negative capital sanctions, 7 types of positive
trade sanctions, and 5 types of positive capital sanctions (Baldwin, 1985, tables 2 and 3:
pgs. 41-42).

While Baldwin clearly provides the broadest and most detailed typology of

economic coercion attempts, a more general classiﬁcation is provided by Hufbauer,

23

Schott and Elliot (1985). The simple typology of Hufbauer, Schott and Elliot has distinct
advantages for empirical studies of sanctions like this one. While Hufbauer, Schott and
Elliot’s typology is not as detailed as Baldwin’s it has the clear advantage of being
operationalized and the data has been made available to empirical researchers. Hufbauer,
Schott, and Elliot state that “There are three main ways a sender country tries to inﬂict
costs on the target country: frrst by limiting exports; second by restricting imports; third
by impeding ﬁnance, including the reduction of aid” (Hufbauer, Schott and Elliot, 1985:
p.28). In general Hufbauer, Schott, and Elliot have far fewer distinctions between types
of sanctions, limiting their analysis to just three types of sanctions and not making a
distinction between positive and negative sanctions. Of the sanction cases that they
quantify, approximately 75 percent of them involved some type of ﬁnancial controls,
while approximately 35 percent involved trade restrictions of either exports or imports.
About 30 percent of the cases they document involved all three types of restrictions,
exports, imports, and ﬁnancial controls.

In chapter 3, I use Hufbauer, Schott, and Elliot’s data to test a theoretical
argument that there is an important distinction between uses of ﬁnancial sanctions and
uses of trade sanctions. In that chapter, I simplify Hufbauer, Schott, and Elliot’s three
categories even further to draw attention to the differences between ﬁnancial sanctions
and trade sanctions.

Before moving on, Baldwin’s argument for including economic means for
economic ends as forms of economic statecraft deserves consideration. He states his case
for considering all uses of economic means as types of economic statecraft forcefully

when he says “...an inﬂuence attempt intended to effect another state’s tariff levels,

24

 

 

economic growth rate, attitude toward private foreign investment, or economic welfare is
a political act.” (Baldwin, 1985:33) Thus, since economic ends can easily be converted to
political ends he includes them as types of economic statecraft. It is somewhat curious
though that Baldwin does place limitations on the means used, even if the ends pursued
are economic. For example, bombing factories to reduce an adversary’s economic
capabilities is not included in Baldwin’s deﬁnition of economic statecraft. But, using
economic capabilities to reduce an adversary’s ability to bomb your factories would be
included. This is a curious exclusion since Baldwin explicitly argues that all potential
policy options available to a state are linked by comparative costs. But in logic typical to
that used by Baldwin, the use of military force could actually be the best form of
economic statecraft available to a policy maker. If bombing an adversary’s factories
lessens their production possibilities, it simultaneously makes them less able to use
economic coercion against your country and makes your country’s economy relatively
stronger thus increasing your future ability to use economic means of coercion. Just as
Baldwin is able to convert all economic ends into political ends, all political ends could
be put into economic ends, or military means could be converted into enhanced economic
means; this is simply a point of reference. Thus, extending Baldwin’s reasoning to its
logical extreme would result in all political acts being types of economic statecraft.
Clearly a boundary must be drawn somewhere. Baldwin draws the boundary at economic
means, here I draw the boundary at economic ends.

As mentioned above, the deﬁnition used here is narrower than Baldwin’s because
I View sanctions as essentially political disputes in which the sender is linking political

Olltcomes to future economic relations. By restricting sanctions to this type of linking

25

behavior, and not including cases of economic means for economic ends, the type of cost
structure associated with sanctions (as deﬁned here) will be higher. Baldwin purposely
maintains the deﬁnitional ends broad and means narrow because his main focus is normal
‘economic statecraft,’ not economic sanctions. Narrowing the deﬁnition, as done here,
implies that sanctions are an ‘abnormal’ use of economic means. In other words
sanctions as deﬁned here are a deliberate and direct attempt to link economic power with
political coercion. I agree with Baldwin that providing subsidies to domestic farmers
discourages foreign imports and might force a lowering of foreign subsidies to their
domestic producers. Thus, state A has used economic policy to affect B’s economic
policy and since economic policy is a product of politics, a coercion attempt has occurred.
However, this type of action is decidedly different from, say, country A announcing
publicly that it will no longer export arms that it produces to a country involved in a civil
war. Or, that it will no longer provide most favored nation status to a country until it
improves its human rights record. Olson (1979) also acknowledges this distinction
between the more subtle nature of economic coercion and the more public form of trade
sanctions about which he says, “ultimately, the objective of most attempts at sanction is
to foster divisions between elements of the elite, or between the elite and the general
populace, or both” (Olson, 1979; p. 474).

An important reason for making this distinction is that without it, it is difﬁcult to
imagine theories applying to costly militarized disputes (as used in this dissertation) also
applying to economic sanctions. Trade disputes, not involving linkages to some outside
political objective, will generally be associated with a smaller cost structure than when

formal sanctions are imposed. Sanctions as deﬁned here signal a clear escalation of

26

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hostilities, breaking trade or rescinding aid, and in many cases attempting to cast the
target as a violator of international norms that must be punished. Therefore, if one were
to align policy options from the least amount of hostility to the most, diplomacy would be
at one end and military force at the other, with economic sanctions (as deﬁned above)
located between the two. Thus, economic sanctions, by themselves, fall short of the
hostility involved in uses of military force, but clearly exceed that of pure diplomacy or
general trade disputes.

As stated earlier, properly deﬁning what a sanction is, goes a long way toward
determining what a successful sanction is. However, a deﬁnition that is too broad
introduces theoretically intractable problems in determining when a sanctions episode is a
success. Allowing for the use of economic means to produce economic ends as part of
the deﬁnition of sanctions means that, by deﬁnition, economic ends become part of the
goal of sanctions and, thus, part of the deﬁnition of success. Restricting the ends of
sanctions to clearly political objectives removes much of the ambiguity introduced by
Baldwin’s deﬁnition. For example, if sanctions are able to produce devastating economic
effects on the Iraqi economy without achieving any of the stated political objectives,
Baldwin’s deﬁnition still allows for them to be successful in terms of imposing economic
costs on Iraq. However, most would argue that UN sanctions imposed on Iraq in 1991
after the Gulf War ended have not been successful because they have not resulted in Iraqi
compliance with UN demands.

Furthermore, there are serious ethical problems with deﬁning success in terms of
imposing economic costs on the target nation. Gordon (1999) investigates the morality of

using economic sanctions to achieve political goals. She argues that sanctions are a form

27

of siege warfare, and because they do not discriminate between innocent civilians and
legitimate military targets, they violate the just war doctrine of jus in bello. The jus in
bello principle of discrimination requires that “the means used for warfare must not be
indiscriminate” (Gordon 1999: 91). This is a powerful critique based on the naive theory
of sanctions that imposing enough harm on civilian populations will cause them to
pressure their leaders to change the policy so that sanctions will be lifted.

However, if one deﬁnes a criterion for success of sanctions, and thus one of the
essential reasons for using them, as inﬂicting economic harm on the target nation, then
sanctions fail to meet the just cause criteria (ius ad bellum) of the doctrine as well. The
jus ad bellum criteria, as deﬁned by Gordon “requires a real and certain danger, such as
protecting innocent life, preserving conditions necessary for decent human existence, and
securing basic human rights” (Gordon, 1999: 1988). If sanctions success is determined
by inﬂicting costs on the target economy without achieving policy changes, then it is
difficult to justify their use under this doctrine because it implies that policy change is not
necessarily the objective. This deﬁnition of sanctions success would be similar to
judging the success of war by the number of buildings blown up and people killed, and
not in terms of the achievement of political goals.

To be fair, it is difﬁcult to attribute policy change to sanctions if there is no
economic harm done to the target. To continue the war analogy, this would be similar to
attributing success to a military campaign that failed to inﬂict any casualties. However,
the true measure of success would be to achieve political objectives while inﬂicting the

minimum necessary amount of collateral damage in terms of civilian harm. Part of the

28

reason Gordon disapproves of sanctions is that she believes minimizing civilian casualties
is neither possible nor even the intention of sanctions.

“. . .the damage done by indirect sanctions is not in fact collateral, in that

the damage to the civilian population is necessary and instrumental. The

direct damage to the economy is intended to indirectly inﬂuence the

leadership, by triggering political pressure or uprisings of the civilians, or

by generating moral guilt from the “fearful spectacle of the civilian dead.”

Sanctions directed against an economy would in fact be considered

unsuccessful if no disruption of the economy took place” (Gordon
1999:397).

However, Gordon’s conclusions would be stronger if not based on a naive model
of the determinants of success. Many researchers focused on the policy aspects of
sanctions as well as those theorists operating in the public choice tradition (as does this
dissertation) have focused on what are commonly referred to as “smart sanctions“. In
both of these areas it is clearly understood that civilian harm is not necessary to achieve
policy success. Even van Bergeijk (1992), who shows an empirical connection to
sanctions that harm the target’s economy and successful sanctions, is clear that sanctions
effectiveness (in terms of economic harm) and sanctions success (in terms of policy
success) are two separate phenomena.

There are yet other problems with. using a deﬁnition as broad as Baldwin’s. His
deﬁnition of sanctions not only demands an evaluation of whether the political and
economic goals were met, but requires the analyst evaluate success based on “the scope of
the inﬂuence attempt, which can include beliefs, attitudes, opinions, expectations,

emotions, and/or propensities to act.” (Baldwin, 1985; p.32) Therefore, if the attitudes,

 

' For an edited volume featuring several authors focused on smart sanctions policy see Cortright,
David and Lopez, George. 2002. Smart Sanctions. Lanharn: Rowman and Littleﬁeld. For sanctions
theorists in the public choice tradition see Kaempfer and Lopez (1988), Kirshner (1992), Bastos ()

29

or opinions or emotions of the target changed favorably during a sanction, it would be
deemed a success even if there were no economic or political effects. Allowing for this
broad of a deﬁnition of success waters down the concept of success to the point where it
is hardly useful.
Threats of Sanctions

As noted earlier, the deﬁnition of sanctions used here is both narrower and
broader than what others have used. It is broader than most in the sense that it is not
limited to actual implementations of economic sanctions. Threatened uses of economic
force to achieve political ends are also included. Baldwin’s deﬁnition, being one of the
more inclusive, also includes threats of economic sanctions,9 but others, like Lindsay
(1986; p.155) explicitly exclude from their deﬁnition, cases where sanctions were
threatened but never implemented. If, as argued in this dissertation, much of the success
of sanctions occurs at the threat stage then ignoring threats is a signiﬁcant omission
leading to an under appreciation for the potential success of sanctions. It is also a
somewhat curious omission since gaining compliance through the threat of sanctions is
the least costly to the sender and thus the truest type of success. Once again it is evident
how the deﬁnition of sanctions can affect opinions regarding the success of sanctions.
Researchers can use a narrow deﬁnition that does not include threats to theoretically
‘deﬁne away’ some of the success of sanctions.

Another important distinction regarding the type of sanctions is whether they were
implemented or merely threatened. The deﬁnition established above deﬁnes sanctions as

uses of economic force to coerce political behavior. As such, if this can be accomplished

30

with the mere threat of a break in trade, then economic force has been used to coerce
political behavior and an argument can be made that a sanction has occurred and
succeeded. On the other hand, if a threat is made and the target of that threat refuses to
cooperate, and the threatening state backs down without carrying out its threat, an
unsuccessful sanction attempt has occurred. Also recall that the most ﬁequently used
deﬁnition of sanctions explicitly includes threats of sanctions, deﬁning them as “. . .the
deliberate government-inspired withdrawal, or threat of withdrawal of ‘customary’ trade
or ﬁnancial relations” (Hufbauer, Schott, and Elliot, 1985: p.2 emphasis on threat added).
The problem for most empirical studies of sanctions has been that threats of sanctions are
generally not documented in a fashion that makes them quantiﬁable. Thus, most previous
empirical studies of sanctions ignore threats, and are thus only analyzing a subset of the
uses of economic force that actually occur.

The extent of the problem introduced by ignoring threats of sanctions depends on
whether selection bias is evident in the observed cases of sanctions. There are sound
reasons to expect that selection bias does occur based on previous theoretical work on
sanctions, and threats in general, which provide clear expectations regarding the
importance of threats and the likelihood of selection bias. The remainder of this section
will review some of the important literature on threats of sanctions.

While there has been much written about threats in general, (Schelling,l960,
1966; Baldwin, 1985; van Bergeijk, 1994, 1995; Hovi, 1998) two major theoretical works
focusing on the threat of sanctions stand out. First, Smith (1997) develops a formal

model that incorporates two elements that were previously given very little attention in

 

9 See Baldwin 1985, p.41 where he lists forms of sanctions and threats of any of the listed actions.

31

the sanctions literature but are important relative to this study. His most important
contribution was the development of a formal model revealing that in many cases of
potential sanctions, they will never occur because one or the other side will back down
prior to the implementation stage. Thus, Smith is able to show that factors affecting the
success of sanctions should inﬂuence their use, and hence that a selection bias is expected
to occur in observed cases of sanctions. This provides a strong theoretical foundation on
which the empirical study of threats performed in later chapters is based.

A second important contribution of Smith’s was to incorporate the costs of
sanctions in the model as being both economic and political. While Smith makes a strong
case for the importance of domestic factors in sanctions decisions, he stops short of
specifying a model that explains how domestic factors inﬂuence sanctions. A logical
extension to Smith’s work is to specify a theory explaining when domestic costs will be
high to the target or sender under different conditions. The economic costs that Smith
mentions have long been studied in the sanctions literature, but the domestic component
of the costs to targets and senders has not been explored. The theory developed later in
this dissertation explores how different domestic institutional arrangements in the target
and sender of sanctions affect the political costs of sanctions and thus their use and
subsequent success or failure. The theory developed here is based on the institutional
explanation of the democratic peace (Bueno de Mesquita et al., 1999) and is presented
later in this chapter.

In Smith’s time independent analysis, he arrives at three separate equilibria.

These outcomes are based on values for the costs of sanctions to the sender and the target

32

V)

Cl

01'

Cor

den?

 

36m

 

 

 

and the potential value of the policy in question to the target relative to the costs of
sanctions. Smith’s three equilibria are summarized below.
Condition 1: If Ca 2 0 (Ca = cost of sanctions for the sender)

The sender has no incentive to sanction and in this equilibrium sanctions are never
observed and the target does not change its policy.
Condition 2: If Cb 2 0mm, (Cb = cost of sanctions to target; Omax = Greatest potential value
of policy to target)

In this case, the sender imposes sanctions, and the target immediately complies
(This is considered to be a successful threat by Smith). The sender instantly gets the
policy concession without ever having to pay the actual costs of sanctions. In this
equilibrium, as in the previous, sanctions are never actually observed. However, unlike
the previous case, the policy is changed.
Condition 3: Given that Ca 5 0 and Omax >Cb

This is the only equilibrium under which sanctions occur. Here, the sender is
deriving some other beneﬁts (Smith proposes increased domestic support) that make the
sanctions beneﬁcial to them (Ca 5 0). Therefore, the sender always sanctions and the
target only resists if the payoff from maintaining its policy is greater than the cost of the
sanctions.

In general, based on Smith’s model, two conditions are necessary to observe
sanctions. First, the sender must beneﬁt from sanctions even if it never receives
concessions from the target. Second, the target values its policy more than avoiding the

costs of sanctions. “Sanctions only occur when the political beneﬁts to nation A

33

outweigh the economic costs of sanctions and when the cost of sanctions is low for B”
(Smith, 1997; p.234).

Smith’s study is important because it presents a formal framework for how cases
might ﬁnd their way into the implemented sanction, successful threat, and the failed
threat categories of the model that will be developed later in this dissertation. Smith also
lays the groundwork for the importance of domestic factors in whether sanctions are
threatened, but as mentioned earlier, he fails to provide a theory for when and how those
domestic factors will be important. Smith also fails to consider how domestic factors will
affect decisions by the target of sanctions. He only considers how domestic demand in
the target for sanctions will increase the likelihood that the sender will impose sanctions.
The theory presented in the following chapters of the dissertation ﬁlls this void by
explaining the importance of domestic factors for targets and senders in uses and threats
of sanctions.

The ﬁnal contribution of Smith’s work to this study is that the formal model
presented in this dissertation follows the convention established in Smith’s model of
using one-sided incomplete information with the target’s value for continuing its policy
cast as private information while the sender’s type is common knowledge.

The second theoretical work on threats of sanctions relevant to this study is
Morgan and Miers (1999). Morgan and Miers argue that the conventional wisdom that
sanctions are ineffective is based on empirical analyses of failed sanctions cases. Because
of this, empirical conclusions reached based on this type of analysis may suffer ﬁom

selection bias.

34

They present this problem as a weakness in the sanctions literature because “only
cases in which sanctions were actually applied have been examined” (Morgan and Miers
l999:1). Thus, “it is distinctly possible that in those cases where sanctions would bring
about a change in the target state’s behavior, the threat of sanctions alone might be
sufficient to produce the desired effect” (Morgan and Miers 199921). This could lead to
only seeing sanctions used in cases where they are least likely to succeed, which would
. produce a serious selection effects problem for empirical analyses resulting in sanctions
appearing to be much less successful than they actually are. However in another work,
Morgan and Schwebach (1997:46) come to the exact opposite theoretical expectation
regarding the empirical effect of selection bias on observed cases of sanctions. They
conclude that “If policy makers are aware that sanctions can rarely have an impact (and
they should be) then sanctions should occur only in those instances in which there is a fair
chance that they would ‘work.’ . . .This would imply that sanctions are even less useful as
an instrument of policy than indicated in most previous empirical analyses” (Morgan and
Schwebach, 1997: 46). This theoretical contradiction in expectations regarding the
inﬂuence of excluding threats of sanctions from empirical analysis, reveals a pressing
need for empirical analysis of the type performed later in this dissertation to investigate
the importance of threats.

Morgan and Miers (1999) also develop formal game theoretic models with
complete and perfect information and one-sided incomplete information. As in Smith’s
(1997) model and this dissertation, the key variables in their model determining the
effectiveness of sanctions are the value of the economic relationship and the value of the

issue under dispute (for both the sender and target). The key ﬁndings of there work are

35

that in the complete and perfect information model, sanctions should never be observed
because being able to look ahead and see the outcome; either the sender or the target
would back down before the actual imposition of sanctions.

When incomplete information is introduced, so that the sender does not know if
the target is of the type that would stand ﬁrm once sanctioned or the type that would back
down, they ﬁnd that any of the game’s four outcomes can be reached in equilibrium").

Threats of sanctions have been considered in theoretical analyses of sanctions
(although very infrequently compared to other questions addressed by sanctions research),
most importantly by Morgan and Miers (1999) and Srrrith (1997), but to this point they
have not been assessed empirically. There are two ways to empirically consider the cases
of sanctions that did not occur. The ﬁrst is to analyze all interstate dyads and use a
“selection model” to determine if factors leading to the initiation of sanctions are related
to the success of sanctions. This was done in an earlier analysis by Nooruddin (2000).
Nooruddin’s analysis demonstrates whether observed rates of success in sanctions are
biased due to a selection effect, but it does not tell us much about the success or failure of
threatened sanctions.

A second way of solving this empirical problem is to actually include cases of
threats of sanctions directly into the empirical data. However, threat data is very difﬁcult

to acquire because it requires the recording of what most consider non-events, such as

cases where sanctions did not occur. To overcome this problem, in chapter 5 I perform

 

10 The four possible outcomes are: 1. Sender achieves her policy goals and Target does not, and no
sanctions are imposed. 2. Target achieves his policy goals and Sender does not, and no sanctions are
imposed. 3. Sender achieves her policy goals after sanctions have been imposed temporarily. 4. Target
achieves his policy goals and the economic disruption created by the imposition of sanctions attains its
maximum level.

36

an empirical analysis on a speciﬁc sanctions policy that has threats built into it. In
chapter 4 I return to this topic and discuss the theoretical issues further before specifying
a formal model of sanctions threats.

Scope of the Influence Attempt (Sanctions Success)

The second component of economic sanctions is the scope of the inﬂuence
attempt. This can also be thought of as the goal to be achieved through the use of
sanctions, or against what standard should sanctions success be measured. There is an
unmistakable link between the goals of sanctions and the success of sanctions. Whether
sanctions are deemed to be successful depends in a large part on how one deﬁnes the
goals of sanctions, and thus the criteria for success. For example, narrowing the
deﬁnition of sanctions so that it only includes uses of economic force to achieve political
changes (as I have in my deﬁnition) makes it less likely that sanctions will be successful.
Success, by this deﬁnition, requires fungibility of power in the sense that economic power
is being used outside the economic realm.

Lindsay (1986) classiﬁes the criteria for successful sanctions by grouping the aims
of sanctions into 5 categories: Compliance: Forcing the target to alter its behavior to
comply with sender preferences. The goal here, according to Lindsay, is to make the
costs exceed the beneﬁts that the target government gets from whatever the policy in
dispute is. Subversion: The goal is to remove the current leadership in the target or
overthrow the entire regime. Deterrence: The goal is to dissuade the target from
repeating a particular action. The sender may also be attempting to dissuade other

governments from taking similar actions or simply to show your ability to inﬂict

 

37

econorrric pain if you want to. International symbolism: The goal is to send a message
to other countries in the system. Domestic symbolism: As in diversionary theories of
war, the goal here is for the sender to increase domestic support or thwart internal
criticism of its foreign policies by acting decisively.

In a similar vein, Barber (1979) organizes the goals of sanctions into primary,
secondary and tertiary objectives. Primary objectives are concerned with the actions and
behavior of the state or regime against whom the sanctions are directed — i.e., the target
state. Secondary objectives relate to the status, reputation, behavior and expectations of
the government imposing the sanctions — i.e., the sender state. Finally, Tertiary
objectives are concerned with broader international considerations, relating either to the
structure and operation of the international system as a whole or to those parts of it that
are regarded as important by the sender state.

However one classiﬁes the goals of sanctions, it is crucial that the goal against
which the success of sanctions is being measured is clear. Looking at the above
classiﬁcation system of the goals of sanctions makes one aware of how varied the
demands on sanctions success can be. There is a big difference between being successful
at “increasing domestic support or thwarting internal criticism of foreign policies” and
“forcing another sovereign state to alter its policy.” This can lead to a great deal of
confusion in determining whether sanctions are a generally successful or unsuccessful
policy.

Both Barber and Lindsay, by explicating the various goals of sanctions, are able to
show that sanctions are more likely to achieve certain goals than others. Thus, sanctions

that do not achieve compliance from the target (primary goals in Barber’s terminology)

38

should not automatically be deemed a failure. Secondary and tertiary goals of sanctions
may be achieved while at the same time exerting pressure that at least keeps the
possibility open for the achievement of primary (compliance) goals.

Perhaps the most commonly used measure of the success of a sanction episode
comes from Hufbauer, Schott, and Elliot’s deﬁnition. They use the following two part
deﬁnition of success “The success of an economic sanctions episode — as viewed from the
perspective of the sender country — has two parts: the extent to which the policy outcome
sought by the sender country was in fact achieved, and the contribution made by the
sanctions (as opposed to other factors, such as military action) to a positive outcome”
(Hufbauer, Schott, and Elliot 1990: 41).

A problem that Drezner identiﬁes with the coding of success in HSE and other
studies is that “success is narrowly deﬁned as the extent to which the policy outcome
sought by the sender country was in fact achievedl 1” (Drezner, 1999; p.107). While this
deﬁnition captures whether the target conceded to the sender’s demand, it omits the
importance of the original demand. Thus Drezner combines Hufbauer, Schott, and
Elliot’s success variable with their demand variable to create a variant, which he refers to
as “concession size.” Drezner prefers this operationalization because he argues, “A
proper measure of concession magnitude should take into account both the size of the
demand and the degree to which the target met the demand” (Drezner, 1999; p. 107).

Perhaps even more importantly, some argue that sanctions have to reach their
stated policy goal to achieve success while others say that sanctions may be successful

without achieving the stated policy goal. For some, sanctions can be deemed successful if

39

they simply impose punishment on the target in the form of economic hardship. Others
(N ossal, Barber) would go even further and say that sanctions can be successful even if
there is no economic hardship imposed (or only economic hardship and no policy change)
as long as the target is identiﬁed as having violated some norm of international behavior.
One of the most serious challenges to the notion that sanctions can be successful
comes from Pape (1997). Pape ﬁnds serious ﬂaws with what he calls the “new
conventional wisdom” regarding sanctions, which acknowledges the limitations of
sanctions but still believes they are often an effective policy instrument for achieving
important policy goals. (Pape, 1997) Pape attacks the idea that sanctions can be
successful on both inductive and deductive grounds. Inductively, he argues that the
primary database used for empirical research (Hufbauer, Schott, and Elliot, 1990) is

‘6

seriously ﬂawed and as a result overestimates the success of sanctions. ...economic
sanctions have little independent usefulness for pursuit of non economic goals. The HSE
study is seriously ﬂawed. Practically none of the claimed 40 successes of economic
sanctions stands up to examination” (Pape, 1997:93). Pape’s inductive critique and
subsequent recoding of the Hufbauer, Schott, and Elliot database to include only 512
success out of 115 cases has received a great deal of attention and must be addressed by
those using the Hufbauer, Schott, and Elliot data for analysis (as I do in chapter 3).

Consistent with what I have presented earlier in this chapter, I argue that the debate

introduced by Pape is really nothing more than a difference of opinion regarding the

 

“ Quoted from HSE (1990), p.41.
'2 Originally Hufbauer, Schott, and Elliot code 40 successes

4o

deﬁnition of an economic sanction and the criteria for their success. Consider this outline
for Pape’s criteria for successful sanctions:
1. The target state conceded to a signiﬁcant part of the coercer’s demands.
2. Economic sanctions were threatened or actually applied before the target changed
behavior.
3. No more credible explanation exists for the target’s change of behavior.

a. And... “Military conquest, when it occurs, is always a more credible
explanation than economic sanctions because the target’s failure to
concede before military defeat is in itself evidence of the failure of
coercion.” (97) If ‘economic pressure’ weakened the target’s military
capabilities and lead to military conquest this should count as a success of
“economic warfare.” But, it is not evidence that the target would have
conceded a policy goal without the presence of the military conquest.

b. Assassination or military coup is also evidence of the failure of coercion
against the deposed government.

The ﬁrst two criteria for success are not substantially different from Hufbauer,
Schott, and Elliot’s deﬁnition for success (presented earlier), but Pape’s third criterion
deﬁnes away many of the successful cases of sanctions found in Hufbauer, Schott, and
Elliot. In Part 3a of his requirements for sanctions success, Pape claims that whenever
military force is used sanctions cannot be successful, and that successful cases of what he
terms “economic warfare” should not be counted as successful cases of economic
sanctions. There is obviously room for disagreement with Pape’s decision to separate
economic warfare from economic sanctions and with his claim that use of military force
necessitates sanctions failure.

For example, just because Germany did not surrender in World War II as a result
of allied attempts to isolate Germany from trade does not necessarily mean that trade

restrictions were a failure. The fact that Germany was unable to obtain adequate medical

supplies and particularly, enough oil with which to fuel its army played a role in the allied

41

victory. Why would trade restrictions, which helped keep Germany from obtaining
adequate medical supplies and oil, not be deemed successful? Gilbert (1989) describes
the effect of dwindling supplies on Germany’s ability to continue the war in the spring of
1 945.

“Germany’s last hope of an adequate fuel supply to make war was

lost. A counter-attack that day in the Ruhr, led by General Student,

had to be postponed because of a lack of tank fuel; this fact was

known to the Allies through Ultra. Not only fuel oil, but essential

medical supplies, were now virtually unobtainable within the

dwindling conﬁnes of the Reich; on April 2 the head of Germany’s

medical services, Dr. Karl Brandt, wamed Hitler personally that

one ﬁfth of all medicines needed no longer existed, and that stocks

of two-ﬁfths would run out completely in two months. (Gilbert,

1989:657)

Thus, contrary to Pape’s argument that these economic restrictions were a failure, I would
cast them as a success.

Although it generally receives less attention, the deductive part of Pape’s
argument is equally interesting. Pape argues that the deductive logic of sanctions theory,
that increased economic punishment will lead targets to change policy, is ﬂawed. Pape
states, “The key reason that sanctions fail is that modern states are not fragile.” (Pape,
1997:106) And following the logic of Galtung’s (1967) ‘rally around the ﬂag effect’ he
says, “Even in the weakest and most fractured states, external pressure is more likely to
enhance the nationalist legitimacy of rulers than to undermine it” (Pape, 1997:107). If
modern states can withstand ﬁrebombing (Germany and Japan during WWII) and
strategic bombing (North Korea, Vietnam, Iraq) without causing their populations to rise

up against the leaders, Pape asks, why should sanctions be expected to accomplish that

task?

42

The Determinants of Successful Sanctions

In the second half of this dissertation I develop and test a model of the success of
sanctions. In this section I review the important elements of successful sanctions found in
previous works and used in my model. Empirical studies have consistently found two
elements of a sanction episode to be essential in determining the success of economic
inﬂuence attempts. These areas are the cost of sanctions, and the value of the policy at
dispute. Not coincidently, these are also the two areas that the previous theoretical
models by Smith and Morgan and Miers focused on and the areas focused on by the
theoretical and empirical model used in this dissertation. Each of these areas can be
subdivided to include the costs and value to the sender and the target. Furthermore, as
hypothesized in this dissertation, a critical element in determining the costs and value of
sanctions are the domestic political arrangements in both targets and senders of sanctions.
Since the deductive model builds on these concepts and the empirical model
operationalizes them and tests their importance, I will discuss some of the results of
previous empirical models that have included each of these variables. Thus, this section
provides the grounding in the literature for the components included in the theoretical
model of chapter 4 and the subsequent empirical analysis of chapter 5.

After reviewing previous ﬁndings on the importance of each of these elements, I
provide a brief summary of the ﬁndings of the empirical chapter on sanctions success
from this dissertation. While the ﬁndings regarding success are generally consistent with
previous literature, they are able to provide valuable new incites regarding the importance
of threats to the achievement of success. The dissertation also tests the importance of

concepts previously found to be important, only using a newly developed data set. This is

43

particularly important for the study of sanctions as virtually all previous empirical studies
of sanctions have tested their hypotheses against the same set of data from Hufbauer,
Schott, and Elliot.
Economic Cost of Sanctions

A fundamental characteristic of a sanction episode that has been related to its
success is the amount of economic cost imposed on the target of the sanctions. Since the
primary tool in economic coercion is to restrict some type of economic activity it is
thought by many that hurting the target economically is essential to the achievement of
success. Van Bergeijk states that “A sanction simply cannot be successful, if for example,
no damage can be done” (van Bergeijk, 1994225). And, moreover, he claims that it
should not be surprising that sanctions should fail when this fundamental theoretical
condition for success is not met. Therefore, if the amount of trade between target and
sender of sanctions is low as a proportion of the target’s GDP we should expect to see
sanctions fail.

Table 2.1: Success and Potential Cost of Sanctions

 

 

 

 

 

 

 

 

Trade Linkage % Successes 1 Failures 2 Success rate %
1/ 1+2
0-1 5 22 19
1-2 6 10 38
2-4 7 11 39
4-10 6 8 43
>10 8 9 47
Total 32 60 35

 

 

 

 

Proportional trade linkage is deﬁned as the bilateral trade ﬂow between sender and target
as a percent of the target’s GDP and is measured in the year prior to the sanction.

The table, from van Bergeijk (1994), shows that sanctions tend to fail if the

proportional trade linkage is not substantial. What is also interesting is that having a

 

substantial trade linkage is not sufﬁcient for success. One reason for this might be that
sanctions, which impose greater economic costs to the target, also impose more potential
harm to the sender’s economy.

In later work van Bergeijk (1995) performs a logit analysis using the Hufbauer,
Schott, and Elliot (1990) data set and ﬁnds that absolute trade linkage (relating the
bilateral trade ﬂow between sender and target to the target’s total trade ﬂow) and
proportional trade linkage (the sender’s trade ﬂows to the target, as a percentage of the
target’s GNP) both are positively and signiﬁcantly related to success.

All of the following authors ﬁnd empirical support for the idea that increased
economic harm to the target leads to greater success of sanctions. Morgan and
Schwebach (Using Hufbauer, Schott, and Elliot’s (1985) cost to sender and cost to target
variables, which are based on economic costs) found that “. . .as the cost to the target
increases, the probability that the sanctioner will obtain a favorable outcome also
increases” (Morgan and Schwebach, 1997: 44). Furthermore, Morgan and Schwebach
found empirical support for the idea that greater economic costs to the sender decrease
the probability of success. “Likewise, as the costs to the sanctioner increases, its ability
to obtain a favorable outcome decreases” (Morgan and Schwebach, 1997: 44)

Drury (Also using Hufbauer, Schott, and Elliot’s (1985) cost variable) in his
‘revisiting’ of Hufbauer, Schott, and Elliot’s empirical analysis found that “. . .the greater
the cost of sanctions to the target, the greater the likelihood they will succeed” (Drury,
1998:508). Also using Hufbauer, Schott, and Elliot’s cost variable, Dashti-Gibson et. al.,
found similarly that “Substantively, the model suggests that sanctions are likely to

succeed the greater the costs imposed...” (Dashti-Gibson et. al., 1997:613). Dehejia and

45

Wood ﬁnd that “the cost of sanctions to the target country. . .are positively related to the
probability of success of sanctions” (Dehejia and Wood, 1992:79). Finally, Hufbauer,
Schott, and Elliot (1990) in their logit analysis of success ﬁnd that “cases that inﬂict
heavy costs on the target country are generally successful.”

Based on these empirical ﬁndings I include economic costs to the sender and
target in the theoretical model of chapter 4 and operationalize the concepts (with data
other than the Hufbauer, Schott, and Elliot cost variables) for testing in the empirical
model of chapter 5. The empirical frndings of chapter 5 are consistent with the ﬁndings
of previous studies. When the expected costs of sanctions are high to the sender they are
less likely to make threats of sanctions and are more likely to back down from the threats
if they do make them. Targets are more likely to comply at the threat stage when the
expected economic costs are high to them and if the sanctions actually are imposed,
higher costs to the target lead to a greater probability of success.

Value of the Policy at Dispute

Empirical support for the effect of the value of the policy at dispute on the
probability of sanctions being successful has been mixed. This is however, not altogether
surprising since if the issue is of great value to one country it is likely to be of great value
to the other country as well. Thus, increasing the value of the policy at dispute increases
the incentive of both sides to prevail in the dispute and the effects are likely to cancel out
in empirical models. Both Nooruddin (2001) and Bolks and Al-Sowayel (2000) included
different policy goals as independent variables in their model speciﬁcations without

ﬁnding signiﬁcant results.

46

However, as Drezner argued in his creation of a success variable that incorporates
the size of the concession it should be harder to achieve major policy concessions from
targets with sanctions than minor ones. Dashti-Gibson et. al., looking at the distinction
between sanctions that are simply designed to punish and those designed to elicit a
substantial policy change, conclude that “the factors which determine the effectiveness of
sanctions depend upon the goal of the sender” (Dashti-Gibson et. al., 1997:615).

Similarly, Dehejia and Wood ﬁnd support for the idea that the size of the demand
matters. They conclude “. . .govemments are more likely to capitulate to sanctions when
.the issue at stake is not of vital national interest and, hence, when the political costs of
doing so are tolerable” (Dehejia and Wood, 1992280).

Based on contingency table analysis and bivariate region models, Hufbauer,
Schott, and Elliot provide the strongest support of the convention that major policy
objectives are harder to achieve than minor ones. They state, “. . .the success rate
importantly depends on the type of goal sought” (Hufbauer, Schott, and Elliot, 1985: 80).
“. . .attempts to disrupt military adventures, to impair a foreign adversary’s military
potential, or otherwise to change its policies in a major way, generally fail” (Hufbauer,
Schott, and Elliot, 1985: 80).

The empirical model of this dissertation includes an operationalization for the
value of the policy at dispute to both the target and the sender. Because of the nature of
the model, coefﬁcients are obtained for the importance of the value of the policy to both
the sender and target individually in the decision to become involved in a sanctions
dispute and the subsequent success of the episode. Although the results are explained in

detail in the later chapter, I will mention here that in the model senders are more likely to

47

threaten sanctions when the issue is important to them, but perhaps realizing the difﬁculty
in achieving concessions when the stakes are high, they are also more likely to back down
from the threat without actually imposing sanctions. Targets, on the other hand, are seen
to be less likely to be coerced with a mere threat when the issue is important to them. In
the end, as in other studies, the value of the policy was not found to have a signiﬁcant
affect on the probability of sanctions being successful.

Domestic Political Factors

Domestic political factors have also been shown to play an important role in
determining the success or failure of sanctions. Most of the work on the importance of
domestic factors to sanctions success has been at the theoretical level. In the theory
developed later in this chapter and the deductive and inductive models presented in later
chapters, domestic factors play an essential role. The theoretical literature on the
importance of domestic institutions is reviewed in the next section. Here, I will review
some of the ﬁndings of existing empirical work.

In one of the ﬁrst empirical analyses of sanctions to address domestic political
considerations, Dehejia and Wood (1992) reanalyzed previous econometric models by
Hufbauer et a1 (1990) and Lam (1990) with an additional variable in the model described
as the “virtual invulnerability to domestic opposition” of the target government. They
reasoned “a regime that is vulnerable to domestic opposition from various sources is
more likely to be in a position where the hardship resulting from sanctions could be
transmitted to the rulers themselves” (Dehejia and Wood, 1992: 74). Dehejia and Wood
ﬁnd full support for these expectations in their empirical model. “The a priori

expectation of the importance of these variables has been fully justiﬁed by the results

48

which suggest that invulnerable governments are intrinsically more likely to resist
sanctions, the intuition being that they are invulnerable to political pressure from the
domestic groups most affected by sanctions'3” (Dehejia and Wood, 1992: 80).

Hart (2000) analyses the importance of domestic institutions in the sender of
sanctions and concludes “The hypothesis that domestic regime type has an impact on the
success of economic sanctions is well supported by this analysis” (Hart, 2000: 280). Hart
argues that because democracies are better able to generate domestic costs for actions
taken in foreign policy, they will be better at using economic sanctions as signals of
resolve. This forces democracies to be more careful in their foreign policy and explains
his empirical ﬁnding that democratic senders are signiﬁcantly more likely be successful
in their use of sanctions.

In a complete reversal of perspective from Hart’s analysis of the sender country’s
domestic institutions, Bolks and Al-Sowayel (2000) analyze the importance of the target
country’s domestic institutions. They argue that target states can play a part in
determining the length and probability of success of sanctions by the countermeasures
that they take to defend against the economic effect of sanctions. Their empirical analysis
supports the hypothesis that domestic systems that have fewer constraints will allow
leaders a freer hand in making policy decisions that can counter the effect of sanctions.
Thus they conclude that "...more closed and autocratic regimes better withstand economic
sanctions pressure as leaders in these systems have more ability to implement

countermeasures autonomously" (Bolks and Al-Sowayel, 2000: 255)

 

'3 The unfortunate thing about this analysis is that the authors do not fully explain how they

operationalize the variable “lnvunerable”

49

Like Bolks and Al-Sowayel, the empirical analysis relating to the success of
sanctions performed in this dissertation focuses on the effect of different domestic
regimes in the target of sanctions. The empirical analysis of the initiation of sanctions
includes the inﬂuence of domestic institutions in both sender and target states, but the
analysis of success focuses on the United States as the exclusive sender of sanctions so
there is only variation in the institutions of the target states. The ﬁndings of chapter 5
show that when the United States is the sender and the target is a democracy sanctions are
signiﬁcantly less likely to occur because both the sender and the target are more likely to
back down at the threat level. This ﬁts well with a Signaling argument, because
democratic targets should be less likely to bluff thus they are more likely to have to back
down when threatened with sanctions. Likewise, when a democratic sender like the
United States meets up with a democratic target that resists the initial threat of sanctions,
it should be more likely to back down since the target is more credible. However, once
sanctions are imposed they are not signiﬁcantly” more or less likely to be successful
against a democracy.

This insigniﬁcant ﬁnding on implemented sanctions is not unexpected giving the
Contrary expectations of theoretical explanations of the importance of democratic
iIIStitutions. On one hand, signaling models expect democracies to be more credible in
their commitments because they face greater audience costs for backing down, thus if
democracies resist threats of sanctions they should be less likely to give in at the
in1plementation of sanctions. On the other hand, if democratic leaders are more

\
" The coefﬁcient on target democracy level indicates that sanctions are less likely to be successful

against democracies, but not at a statistically signiﬁcant level.

50

susceptible to economic harm imposed by sanctions because they are held accountable to
their public then democracies Should be more likely on average to give in once sanctions
are implemented. The insigniﬁcant ﬁnding may simply be a result of these two factors
canceling each other out.
Stability of the Target Government

Another way that Domestic factors have entered into empirical studies of
sanctions success is through the stability of the target government. I incorporate this
concept in the empirical model of chapter 5 by including a variable that indicates the
presence of internal conﬂict. The presence of internal armed conﬂicts while not
necessitating regime instability should generally be associated with higher levels of
instability. Most other empirical studies have operationalized this concept with the
highly subjective Hufbauer, Schott, and Elliot indicator of the level of distress in the
target regime. Hufbauer, Schott, and Elliot’s indicator is ordinal with three levels of
distress as follows:
Distress: “a country with acute economic problems, exempliﬁed by high unemployment
and rampant inﬂation, coupled with political turmoil bordering on chaos”
Signiﬁcant Problems: “a country with severe economic problems, such as a foreign
exchange crisis, coupled with substantial internal dissent”
Strong and stable: “a country with the government in ﬁrm control and an economy
experiencing only the normal range of inﬂation, unemployment and similar ills”
(Hufbauer, Schott, and Elliot, l985:3 7).

Based on the use of the Hufbauer, Schott, and Elliot indicator, Bolks and Al-

Sowayel conclude from their empirical analysis that “the target’s political structure and

51

regime stability are determinants of sanction duration15 ” (Bolks and Al-Sowayelz261)
Their primary empirical ﬁnding is that "the target state's features, especially its
institutional structure and the political vulnerability of its regime, signiﬁcantly impact the
duration of sanctions episodes" (Bolks and Al-Sowayelz24l). Also using Hufbauer,
Schott, and Elliot’s indicator, Drury found that “distressed targets tended to succumb to
sanctions more often than strong, healthy targets, but this relationship was marginally
signiﬁcant at best” (Drury, 1998:507).

Drezner, in his Boolean analysis of sanctions found that “If the sanctions impose
signiﬁcant costs on the target regime, then that regime’s domestic stability is a crucial
factor in determining the outcome” (Drezner, in Chan and Drury, 2000:228). Finally, van
Bergeijk (1995) also ﬁnds in his logit analysis of sanctions cases between 1946 and 1989,
that sanctions against politically unstable targets are more likely to be successful. (van
Bergeijk, 1995: 89)

The empirical analysis performed in this study found that internal conﬂict in the
target had a signiﬁcant impact on threat behavior, but was not signiﬁcantly related to the
likelihood of successful sanctions. Targets embroiled in internal conﬂicts were found to
be signiﬁcantly less likely to back down at the threat of sanctions. However, the United
States was also found to be signiﬁcantly less likely to back down after it made a threat
against a nation involved in internal conﬂict. While threats were no more or less likely to
be made against states involved in internal conﬂict, the threats made were more likely to

be carried out.

 

'5 They equate shorter durations with greater success.

52

Value of the Existing Relationship Between Target and Sender

The ﬁnal issue to be considered regarding its affect on the success of sanctions is
the existing relationship between senders and targets of sanctions. Drezner has argued
that sanctions tend to work when they are imposed against allies, not expecting future
conﬂictual relations. He argues that adversaries care about the long-run implications of
making concessions because they anticipate future clashes and are thus, reluctant “to
behave in ways that could strengthen the sanctioner’s future bargaining position and
establish their own reputation for acquiescence.” (Drezner, 1998: 2) Adversaries prefer
short-term economic losses to long-term harm to reputation. On the other hand, sanctions
imposed against allies or friends are rarer, but far more successful” (Drezner, 1998z2).
According to Drezner, allies have lower expectations of future conﬂict, so they will be
less concerned about the reputational consequences of acquiescing.

Hufbauer, Schott, and Elliot operationalize a variable indicating the value of the
relationship between senders and targets based on prior between the countries. This is an
ordinal variable taking on the following values:

Antagonistic: “the sender and target countries are in opposing camps”

Neutral: “the sender country does not have strong ties to the target, but there is a
workable relationship without antagonism”

Cordial: the sender and target countries are close friends and allies” (Hufbauer, Schott,
and Elliot 1985:38).

They explain the importance of this concept to sanctions behavior in the following
manner “Against belligerent countries, forceful sanctions may be needed to coerce the

target government into yielding, especially since the domestic political consequences of

53

“backing down” can be damaging. On the other hand, a friendly country will often
consider the importance of its overall relations with the sender country before fashioning
a response to economic sanctions” (Hufbauer, Schott, and Elliot, 1985: 37). Similar to
Drezner’s conclusion regarding the importance the value of the existing relationship,
Hufbauer, Schott, and Elliot ﬁnd that “Economic sanctions seem most effective when
aimed against erstwhile ﬁiends and close trading partners. By contrast, sanctions directed
against target countries that have long been adversaries of the sender country, or against
targets that have little trade with the sender country are generally less successful”
(Hufbauer, Schott, and Elliot, 1985: 84). In one additional study of note, Dehejia and
Wood also ﬁnd that cordial prior relations between sender and target nations increase the
likelihood of success. (Dehejia and Wood, 1992: 79)

The empirical ﬁndings in the dissertation were similar to the ﬁndings of these
previous studies. Several different variables were used to capture the economic and
political elements of the value of the existing relationship. First, senders were found to
be less likely to threaten those countries with whom they have valuable existing economic
relationships, and targets whose trade with potential senders makes up greater amounts of
their overall GDP are more likely to back down when threatened. Politically, the value of
the existing relationship is operationalized in this study through regime similarity. Thus I
am only able to partially test this aspect of the relationship because the sender is always
the United States and always a democracy. Therefore regime similarity is seen whenever
the target is a democracy. As noted earlier, the United States is less likely to sanction a
fellow democracy, but unlike the ﬁndings of the three previous studies reviewed cordial

relations do not make sanctions any more likely to be successful.

54

However, there is another variable included in the empirical analysis that also
captures the theoretical concept of the value of the existing relationship. Given the
importance of the United States’ political and economic relationship with regional
countries the value of the relationship between the United States and countries in South
and Central America should be very high. Therefore, as in previous studies we would
expect the United States to be more likely to be successful in its economic inﬂuence
attempts with these countries. This is exactly what we see in the empirical analysis. The
United States is more likely to threaten these countries, and less likely to back down after
making a threat. Furthermore, the United States is more likely to see success at both the
threat and implementation level against these countries.

Domain of Inﬂuence Attempt (Sanctions Theory)

The importance of domestic factors in determining the success of sanctions is also
an important area of consideration from a purely theoretical perspective. Theoretically,
this issue deals with the domain of the inﬂuence attempt, or who the sanctions are
designed to hurt. Thus, as in studies of war, considering domestic factors forces analysts
to open the ‘black box’ of the state and devise theories of how different types of states
will respond to economic coercion attempts.

Traditionally, scholars studying economic sanctions have focused on the ability of
sanctions to affect the target country’s economy as a whole and equated that with the
ability to achieve policy goals. This logic is typically referred to as the Instrumental or
Naive Approach to sanctions study. "The greater the economic pain inﬂicted, the more
the political gain realized. According to this theory, when sanctions exercise sufficient

"bite," citizens in the targeted country will exert political pressure on their government to

55

force either a change in policy or a removal of wrongdoers from ofﬁce" (Cortright and
Lopez, 2000219). What this theory is lacking is an explanation of the mechanism by
which economic hardship is translated into political change. Cortright and Lopez aptly
criticize this logic as assuming a “societal transmission belt” able to smoothly convert
economic costs into political change.

In the next section, the main theoretical argument of the dissertation will be
presented to show the importance of domestic importance of different domestic political
institutions in the use and expected success of sanctions. This theory brings together the
public choice theory of sanctions and an institutional theory of the democratic peace from
the conﬂict literature.

Domestic Institutions and Economic Sanctions: An Institutional Explanation of
How Sanctions Work

Previous research on domestic institutions and the use of economic sanctions
ﬁnds its logic in what can broadly be deﬁned as the public choice approach to the study
of economic sanctions. The logic of the public choice approach as applied to sanctions is
most clearly and thoroughly presented in the works of Kaempfer and Lowenberg (1988a,
1988b, 1989, 1991, 1992, 1995). Kaempfer and Lowenberg describe this ﬁeld in
economics in the following way “Public choice economics explores state behavior from
the perspective of rational, individual decision-making on the part of participants”
(Kaempfer and Lowenberg, 1994; p. 105). Kaempfer and Lowenberg’s work is theoretical

in nature and focuses on both the sender and target of sanctions. They argue that

56

government policies, including economic sanctions, are the result of an endogenous
process in which the redistribution of domestic wealth is a key factor”.

Although Kaempfer and Lowenberg are not studying the importance of
democratic institutions on the use of economic sanctions, there are two conclusions from
their body of work that take on great importance for those interested in the inﬂuence of
domestic institutions on the use of economic sanctions. First, they show that the sender
. nation’s sanctions policy will arise out of competition between domestic groups in the
sender country. The type and level of sanctions will not necessarily be the most
economically efficient policy, but will be the policy that satisﬁes the strongest domestic
interest group. Second, in order for sanctions to be successful, they show that they must
hurt supporters of the policy in the target nation at greater levels than they hurt potential
opposition to the policy. Sanctions that hurt both supporters of the policy and opposition
groups in the target at equal levels will result in a reduced national welfare, but will not
lead to a decrease in the level of the policy supplied.

Kaempfer and Lowenberg note that most economists who have studied sanctions
have concluded that they are not very successful because they are unable to impose
enough economic harm on the target. They identify this group of researchers under the
instrumental approach to sanctions success. The logic of the instrumental approach
“relies on massive economic pressure being applied and suggests that only the most

imposing economic bullies will have much of a chance at being successful sanctioners"

(Kaempfer and Lowenberg 1994: p.103).

 

'6 They cite Stigler (1971) and Peltzrnan (1976) as providing the economic foundation on the

economics of regulation that they build on in their analysis.

57

This “instrumental approach” is similar to what Galtung (1967) describes as the
naive theory of sanctions. Naive theory, as described by Galtung, suggests simply that the
more value deprivation sanctions can bring to a country the greater the amount of political
disintegration will result. Galtung is critical of this theory because he says it disregards
the simple theory of adaptation. His main point is that value deprivation creates the
social conditions under which much more sacriﬁce is possible so that the limit for
political disintegration will be reached much later than what naive theory would predict.
This occurs because “human beings and social systems change when exposed to crisis“
(Galtung, 1967: p.407). Galtung says that a society, when worked upon by the forces of
cohesion, may draw on reservoirs of strength and ability not only to resist stress but also
to act creatively -- qualities that lie latent in quieter periods. However, Galtung theorizes
that this will not last indeﬁnitely because there will be a breaking point at which citizens
will no longer wish to do without.

Part of the problem with achieving success in the use of sanctions according to
those who take the instrumental approach is that there are many alternative sources of
trade for a country. Because of this, the more that sanctions "bite" in terms of reducing
the target's terms of trade the greater the opportunity for gain to any country willing to
maintain trade with the target. This explains why there are so many countries willing to
bust sanctions and trade with the target even when the countries may be political enemies.

However, the biggest problem for the instrumental approach, according to
Kaempfer and Lowenberg, is that the instrumentalists do not identify the mechanism for
how the economic damage brought about by sanctions is transferred into political change

in the target. Public choice theory helps identify the mechanism that translates economic

58

sanctions to political change. Their counter-intuitive ﬁnding is that “the impacts of
sanctions on policy processes in the target country are not primarily channeled through
the economic damage created by sanctions" (Kaempfer and Lowenberg, 1999: p.46).
Further, they state that "We will identify a direct link between sanctions and policy
change in the target which bypasses the economic effects altogether, and which helps to
explain the surprisingly strong political effects of sanctions that do not have major
economic effects" (Kaempfer and Lowenberg, 1999: p.46).

Kirshner (1997) presents a theoretical treatment of the success of economic
sanctions in the public choice tradition. Like Kaempfer and Lowenberg, Kirshner argues
that the success of sanctions depends not on how much the target country is hurt
economically, but on how much the right groups within the target are hurt. Kirshner
identiﬁes two groups within the target of sanctions that must be identiﬁed if maximum
pressure is to be exerted on the target government. These groups are "the central
government, and the core groups whose political support allows the regime to remain in
power" (Kirshner, 1997; p.42). “The greater the sanction hurts the central government
directly, the greater the chance is that it will inﬂuence policy. Beyond this, the success of
sanctions depends on whether or not core support groups are affected” (Kirshner, 1997;
p.46).

While there are many similarities between the logic of Kirshner’s explanation and
that of Kaempfer and Lowenberg there is an important, but subtle, distinction that can be
made. Kaempfer and Lowenberg build their theory on the dichotomy between supporters
and opposers of the sanction policy in the target and the sender of sanctions. They argue

that a successful sanctions regime must hurt supporters of the policy in the target more

59

than they hurt opposers of the policy. Kaempfer and Lowenberg’s work is inﬂuenced by
the experience of the UN sanctions against the apartheid policies of the South African
government and they use this case to illustrate how their theory works in practice. In
order to reduce the level of the objectionable policy (apartheid in South Aﬁica), the result
of the sanctions must be that the black minority in South Aﬁica (the opposer of the
policy) is made stronger in relation to the government (the supporter of the policy). If
both groups are hurt equally, there should be no change in the overall level of the policy —
only a resulting decrease in the overall welfare of both groups.

Kirshner’s distinction is between those who have the power to change the policy
(the government and its core group of supporters) and those who do not (everyone else in
the country). Thus it does not matter if a minority group who opposes the policy is made
to gain relative to those who support the policy as long as the opposing group is unable to
inﬂuence political decisions in their country.

There is a building consensus in the sanctions literature regarding the importance
of domestic interest groups in the success of sanctions”, but it does not provide a
theoretical explanation for the identiﬁcation of which groups in a target nation will be
important to leaders in different types of countries. While these works provide the
theoretical underpinnings for a theory of the importance of domestic institutions to
economic sanctions, they lack an explanation for which segments of the population will

make up the “core support groups.”

 

'7 Along with the above-cited literature on the subject, see Becker’s (1983,1985) work on the
importance of pressure groups, and Bonetti (1997) and Spindler (1995).

60

It is at this point that I turn to a theory developed to explain the empirical ﬁndings
of the democratic peace, which focuses on identifying the core group of supporters in
different countries with different institutional arrangements.

Bueno de Mesquita et al’s (1999) institutional explanation for the democratic
peace is based on the following logic.18 First, the assumptions supporting the theory are
that leaders will choose the policy and effort that maximizes their expected utility and the
primary goal of all leaders, democratic or autocratic, is to remain in ofﬁce. Building on
these assumptions, they posit that the sizes of two political institutions, the winning
coalition and selectorate, explain the variation in foreign policy across states. The
winning coalition is the group of people that must be satisﬁed in order for any leader to
be reselected to office, while the selectorate is the group of citizens who possess the
ability to choose the leader. When the winning coalition for reelection is large (as in a
democracy), leaders will try to keep as many services as possible available to the voting
public. When the winning coalition is small (autocracy), leaders will try to keep as many
resources as possible channeled to a small group of elites making up the winning
coalition. This follows because autocratic leaders are able to satisfy their smaller winning
coalitions through the distribution of private goods enjoyed speciﬁcally by members of
the winning coalition. Conversely, in democracies the winning coalition is larger (usually
over 50% of the selectorate) so the amount of private goods enjoyed per member of the

winning coalition is much smaller than in an autocracy. Therefore, public goods attained

 

'8 The institutional explanation of the democratic peace is not necessarily superior to the cultural
eXplanation. Rather, as argued by Russett and Oneal (2001) the institutional and cultural “explanations are
really complementary: culture inﬂuences the creation and evolution of political institutions, and institutions
shape culture” (2001: 53).

61

through successful policy are relatively more important in democracies versus
autocracies.

Linking the logic of the public choice perspective of econorrric sanctions with this
institutional explanation for international conﬂict provides a fuller explanation of the use
of economic sanctions than can be achieved with either separately. The public choice
argument tells us that sanctions have to satisfy a core group of domestic supporters in the
sender while simultaneously hurting the core group of government supporters in the target
of the sanctions. The institutional theory provides valuable cues regarding the size and
composition of these core groups based on the different institutional rules for the
selection of leaders.

A Public Choice Example

While the following chapter provides large N empirical tests of the institutional
theory of sanctions, shows how some of these ideas function within a single case of
economic coercion. Bastos (1994) provides valuable incites on how threats of United
States sanctions worked against Brazil in the mid 1980’s over Brazil’s protectionist
information technology policies. Bastos makes two points that are central to this study.
First, she emphasizes the importance of threatened sanctions. "I contend that the threat of
economic sanctions was the central strategic tool that moved the players to the conclusion
of the conﬂict" (Bastos 1994; p.381, emphasis on threat added). Second, she contends
that the effectiveness of the threat of sanctions was not only related to the economic
losses that they could have caused but also to the "political effects upon previously
uncommitted interest groups within Brazil" (381). It is for this reason that I include

Bastos in the public choice tradition. She clearly is not taking an instrumental or naive

62

approach, since she attributes the success of the United States threats not to economic
losses but to mobilizing certain interests groups within Brazilian society.

These two points are crucial because She argues that the threat of sanctions caused
contention to develop between different political groups within the country. Groups that
were previously united were now pitted against each other and distinctions were drawn
that were not previously made drawing previously uncommitted groups into the battle.
She states, "While opposition to the policy grew within the state apparatus, dissatisfaction
with the policy within the private sector was mounting. These two trends had a
signiﬁcant impact on the position of the Brazilian government and particularly SE1 (SE1
is the Special Secretariat for Informatics, the Brazilian agency in charge of implementing
the IT policy). The Brazilian 301 case added powerful new critics to the group of
industrialists in opposition to the policy.

"Producers of industrial goods such as orange juice, shoes, textiles,
plywood steel products, and aircraft, whose interests and activities had
never before been disturbed by the policy, were led to oppose it
because of the American threats of economic sanctions against their
exports" (394). "Thus for the ﬁrst time in the IT policy's short history,
the conﬂict pitted the local IT sector against the whole Brazilian
economy" (394).

This is exactly what the theoretical approach I have outlined would predict is
necessary to get policy changed. Sanctions have to sway inﬂuential supporters of the
regime to oppose the policy. It is not as important to consider whether sanctions help or
hurt the existing supporters or opposition to the policy. Rather, sanctions must sway
undecided and inﬂuential members of the target to oppose the policy in question.

Bastos’ conclusions clearly support this idea. She states that "The effectiveness of

the threat of trade sanctions against Brazilian exports to the United States market was not

63

due. . .to the concentration of losses on groups beneﬁting from the IT policy. Rather it

was because the sanctions were directed to previously uncommitted interest groups"

(383)

64

Chapter 3
The Initiation Of Sanctions19

A large body of research in international relations deals with the effect of
domestic institutions on inter-state relations. One rather ubiquitous ﬁnding is that there is
a certain and separate peace that exists between pairs of states with democratic
institutions that does not exist between pairs of states where one or both states do not
have these institutions in place.20 What is less clear, however, is the effect democratic
institutions have on the initiation of lower levels of coercive force, like the initiation of
economic sanctions, to achieve political goals.

This portion of the analysis is motivated by the question of whether domestic
institutions will have an affect on the use of economic force that is consistent with their
inﬂuence on uses of military force. For instance, are countries with certain political
systems more likely to impose economic sanctions than countries with other types of
political systems? Will democracies avoid using economic sanctions to coerce fellow
democracies? Are some political systems more or less susceptible to economic coercion?
The answers to these questions are important to the study of international relations and
more speciﬁcally to the importance of domestic institutions on international conﬂict.
Given the overwhelming empirical evidence that two democracies will not engage in
(serious) military conﬂict, this analysis seeks to understand if these ﬁndings hold up at

lower levels of conﬂict like in the use of economic sanctions.

 

'9 This chapter of the dissertation has undergone substantial revisions and is currently under review for
publication as a separate coauthored article with Mark Souva.
2° The literature on the democratic peace is now too numerous to cite all relevant pieces. Recent

reviews of this research program include Russett and Starr (2000) and Ray (1995, 1998).

65

Scholars studying military conﬂicts have explored the effect of regime types on
the initiation, duration, severity and outcomes of wars. Until very recently, this has not
been the case for those studying the use of economic force. Kim Richard Nossal, a
leading sanctions researcher, notes that scholars studying sanctions “. . .have tended to
overlook one important variable: the impact that regime type has on the success or failure
of international sanctions as an instrument of global governance” (N ossal 1999; p.127).
Thus, this portion of the study builds on and extends the small, but growing, body of
work on the effects of domestic institutions on the use of economic force (Bolks and Al-
Sowayel, 2000; Dehejia and Wood, 1992; Dorussen and Mo, 2001; Hart, 2000; Kaempfer
and Lowenberg, 1988; Kirchner, 1997; Lektzian and Souva, 2001; Morgan and
Schwebach, 1996; Nossal 1999).

In addition to their role in the use of military force, it is argued here that domestic
institutions also inﬂuence the use of and responses to economic coercion attempts. I
extend Bueno de Mesquita, Morrow, Siverson, and Smith’s (1999) institutional theory of
the democratic peace to the use of economic sanctions. Based on this theory, I also derive
additional logical expectations for behavior between states regarding the use of economic
coercion.

Further, I argue and ﬁnd, that the institutional structure of democracies leads them
to use sanctions as a foreign policy tool more often than other types of political regimes.
However, while democracies use sanctions more, an emphasis on successful foreign
policy and greater sensitivity to audience costs make a democracy less likely to impose
sanctions on another democracy. The institutional differences between democracies and

autocracies also provide an incentive for these regime types to use different types of

66

sanctions. Democracies, being more responsive to broad domestic interests, prefer to
impose more targeted ﬁnancial sanctions as they cause less harm to the sender's general
public. I also ﬁnd that the greater incentive in a democracy to have more successful
public policies leads democracies to pursue minor goals more often than autocracies. In
short, this research provides an analog for the democratic peace proposition substituting
the use of economic force for the use of military force.

Bueno de Mesquita et al derived several testable implications regarding regime
types and war from their institutional theory of state behavior, all of which are consistent
with the empirical ﬁndings of the democratic peace research program. In the paragraphs
that follow, I extend the logic of the institutional theory to the use of economic sanctions,
as deﬁned above. I also discuss the empirical implications of these extensions and state
them as testable hypotheses.

Monadic Analysis

In the same way that the democratic peace encompasses both dyadic and monadic
expectations and empirical regularities, I argue that the inﬂuence of domestic political
institutions on the use and effect of economic sanctions also has monadic expectations.
More speciﬁcally, the inﬂuence of the size of a nation’s winning coalition and selectorate,
and in turn, a state’s relative emphasis on public or private goods also impacts the use and
type of economic sanctions. As mentioned earlier, democracies have larger winning
coalitions than autocracies. Given the diversity of preferences and plethora of interests in
any particular society, a large winning coalition will be the sum of many interest groups.
Kaempfer and Lowenberg argue that “sanctions are designed speciﬁcally to beneﬁt

interest groups in the sanctioning countries,” and that “maj oritarian democratic politics

67

will tend to overproduce such special interest legislation” (Hansen, 1992: 43). Similarly,
Hansen has shown that “industries seek regulation in order to increase their wealth,” and
that a “regulator will seek to satisfy politically strong groups in society at the expense of
politically weak groups” (Hansen, 1990: 26, 27). The above argument, combined with
the simple assumption that all leaders, democratic or autocratic, seek to remain in office,
results in democratic leaders having greater motivation to use sanctions as a foreign
policy tool than their autocratic counterparts. In other words, the relationship between the
winning coalition and particular interest group pressures motivate the imposition of
sanctions against other nations. Compared to democracies, autocracies have smaller
winning coalitions and correspondingly fewer interest groups to satisfy. Thus, autocratic
governments are expected to use sanctions less than democratic regimes. This leads to
the ﬁrst monadic hypothesis: Democracies impose sanctions more often than other
regime types.

While the theoretical model leads to the expectation that democracies will use
sanctions more often, it also predicts that democracies will be more sensitive to potential
welfare losses brought about by the use of sanctions. This leads to speciﬁc expectations
regarding the type of sanctions a state will impose given the Size of a state’s winning
coalition and selectorate. All trade sanctions, whether import or export, generate welfare
losses for both the sender and the target. A brief analysis of the welfare effects of
sanctions illustrates this point.

When sanctions are imposed, closing off the exportation of goods, a surplus is
enjoyed by consumers of exportable goods, resulting in a reduction in the price of

exportable goods. Therefore, consumers of exportable goods realize a net gain while

68

producers of exportables do not fair as well. When sanctions prevent imports, supply lags
behind demand and price rises. This results in a loss to consumers of imports. However,
at the new, higher price, domestic producers of imported goods stand to gain by
increasing their production and selling at a higher price”.
This analysis of the welfare effects of sanctions applies to both senders and targets
of sanctions and points to the inescapable conclusion that sanctions should bring about
welfare losses, not only in the sanctioned country, but also in the sanctioning country.
Table 3.1 summarizes these expectations for initiators of export and import sanctions.

The table illustrates that there will be domestic winners Grighlighted in bold) and

domestic losers (in bold italics) for any trade sanctions imposed.

 

2' See Black and Cooper (1987) for a more detailed discussion.

69

 

 

 

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70

Since the welfare losses will be felt across broad crosscutting domestic coalitions,
it is expected that a democracy would rather not impose either export or import sanctions
as a rule. Additionally, if one considers sanctions as trade restrictions that distort normal
trade ﬂows, allowing leaders to divert rents to political supporters, I would expect
autocracies to use trade sanctions more than other types of regimes.

So how do democracies deal with these contradictory internal forces, one force
pushing them toward the use of sanctions to satisfy domestic interest group pressure and
the other pulling them toward the welfare beneﬁts of free trade? The answer lies in the
fact that all sanctions policies will not affect a democratic leader’s core group of
supporters equally. Thus, democratic leaders will constantly seek a policy option able to
simultaneously satisfy the demand of interest groups for action while not harming their
domestic business commrmity. One such option lies in the use of what have been referred
to as ‘smart sanctions,’ and which include the use of ﬁnancial sanctionszz. The primary
advantage of ﬁnancial sanctions is that compared to the trade sanctions described in table
one, which alternately hurt domestic consumers and producers, ﬁnancial sanctions have
relatively few domestic costs to the sender. Hufbauer, Schott, and Elliot are clear
regarding the expectation of the diminished costs of ﬁnancial sanctions, “In the ﬁrst
place, if the ﬁnancial sanctions entail a reduction in ofﬁcial aid or credits they are not
likely to create the same backlash, ﬁ'om business ﬁrms at home and allies abroad, as the

interruption of private goods” (Hufbauer, Schott, and Elliot, 1985: p.59).

 

22 Cortright and Lopez (2002) is an edited volume providing a thorough treatment of this subject.

71

This is not to say that ﬁnancial sanctions are without high costs to their targets.
This is particularly true in terms of the long-term effect of ﬁnancial sanctions on a target
country’s ability to attain future credit. Hufbauer, Schott, and Elliot are also clear about
this expectation when they say “. . .private ﬁnanciers who might provide an alternative
source of credit [to the target] must anticipate a longer-term relationship with the target
country, and long-term relations are unsettled when ﬁnancial sanctions are in the air”
(Hufbauer, Schott, and Elliot, 1985: p.59).

States have a broad array of types of sanctions that can be used, all of which ﬁt the
deﬁnition of economic sanctions in that they use economic means to extract political
change from a target. Kirshner identiﬁes ﬁve types of economic sanctions: “actions
which disrupt trade, aid, ﬁnance, currency, and assets of the target state. Trade sanctions
are further broken into export and import sanctions. There is a clear distinction between
trade sanctions, of which the negative welfare effects were just discussed, and the other
four types of sanctions (which I group together as ﬁnancial sanctions), which are unlikely
to produce negative welfare effects in the user of the sanctions. Because of this
distinction I argue that democracies will use sanctions more often than autocracies (as a
response to domestic pressure), but the particular sanctions instrument they choose is
more likely to be ﬁnancial sanctions.

Autocracies, on the other hand, are more likely to use trade sanctions. Trade
sanctions have the dual beneﬁt for an autocracy of pressuring the target nation to change
an unwanted policy while at the same time allowing the autocratic leader to manipulate
the rents of the trade distortions to beneﬁt key groups of supporters domestically. This

leads to the second monadic hypothesis: Democracies prefer to impose ﬁnancial

72

sanctions alone, rather than combinations that include import or export sanctions. By
contrast, autocracies will be less reserved in their sanctions policy, preferring
comprehensive packages of sanctions including both import, and export sanctions.

The institutional differences that lead democracies to focus on the public health
and welfare of their public also make democracies more likely to pursue minor political
goals via sanctions. Because democratic leaders are more likely to be punished for failed
public policy, they have to be careful about selecting the type of disputes over which to
impose economic sanctions. Since eliciting a major policy change from a target nation is
expected be more difficult than coercing minor policy changes, democracies are expected
to be less likely to attempt sanctions over major policy goals than autocracies.

Therefore, although democracies are more likely to impose sanctions, they will be less
likely than autocracies to impose sanctions over a major policy issue. This leads to
monadic hypothesis three: Democracies are more likely than autocracies to impose
minor sanctions.

Dyadic Analysis

Although the democratic peace involves a number of monadic regularities, its
most robust support is at the dyadic level. Indeed, the “democratic peace” is a dyadic
phenomenon. Jointly democratic dyads are signiﬁcantly less likely to experience war or
other less intense forms of militarized conﬂict than other types of dyads. I argue that the
same reason democracies rarely, if ever, ﬁght wars with other democracies also makes
them less likely to impose sanctions on democracies compared to autocracies.

The institutional explanation says that since democracies are responsive to a larger

constituency, when they are engaged in wars they will try harder to keep their country

73

from experiencing losses brought about by the war effort. This makes democratic
countries less likely to ﬁght wars with other democratic countries for two reasons. First,
democratic leaders need to enact successful policies to remain in ofﬁce. Second, a
democratic leader knows that other democratic leaders also need to conduct successful
policy. These two effects work together to make it extremely rare, but still possible, for
two democratic countries to ﬁght a war. In other words, a democracy makes a very poor
target for another democratic state.

Another implication of these two points that leads to the same conclusion is that
democratic institutions facilitate the commrmication of resolve, thereby increasing the
likelihood of settling a dispute, economic or military, in the negotiation phase. Strategic
models of international relations characterize disputes between nations as a process
(Bueno de Mesquita and Lalman, 1992; Fearon, 1994; Schultz, 1998, 1999). Since there
are always potential disputes between nations, owing to different preferences on any
number of issues, this formulation of disputes as a process begs the question of why some
disputes escalate and some disputes do not? The general answer given by strategic models
is that disputes escalate due to uncertainty surrounding the resolve of a nation. Factors,
such as democratic institutions and the accompanying audience costs that enhance the
ability of an actor to signal resolve reduce the likelihood of a dispute escalating.
Therefore, once again, jointly democratic institutions make it extremely unlikely for a
dispute to escalate to war.

I argue that the factors, a greater reliance on successful public policies and a
greater ability to send more clear signals, that make democracies less likely to engage in

warfare also make democracies less likely to impose economic sanctions against one

74

another. While I believe the war analogy identiﬁes the appropriate causal mechanisms,
the analogy is imperfect. The cost of using sanctions is lower than the cost of warfare.
However, since the costs involved in economic sanctions are less than in military
conﬂicts, the correlation between joint democracy and the use of sanctions is not
expected to be as perfect as the correlation between joint democracy and the use of
military force. But, if the theoretical linkages of the institutional democratic peace are
correct they should continue to operate during economic coercion attempts

As in uses of military force, given the inherently strategic nature of international
relations, initiators of economic force must consider the likely response to their actions.
Thus, the leader of a democratic country may consider using economic sanctions to attain
successful public policy, but is not likely to reach the point where it puts sanctions in
place against another democracy. There are two reasons for this. First, the public choice
perspective tells us that sanctions need to hurt the core group of government supporters to
be successful. The institutional logic tells us that the core group of supporters for a
democratic leader will be large and most efficiently supplied by successful broad based
public goods. Therefore, the target of sanctions, being a democracy, will take all
necessary means to offset or counter the sanctions in an effort to continue providing a
stream of public goods to members of its broad winning coalition. If the target effectively
counters any negative effects of the sanctions, the sender will be perceived to have
initiated a failed policy and will suffer domestic costs. Thus democratic leaders who rely
on successful public policies to satisfy their winning coalitions are not likely to use

sanctions against other democracies.

75

The second explanation focuses on the costly signaling argument made above.
The realization of a sanctions episode requires both that the sender of sanctions decides to
sanction and the target decides to let itself be sanctioned (i.e. it decides not to comply
with the demands of the sender). In other words, the decision to implement sanctions is a
process during which negotiations occur. If the target decides to allow itself to be
sanctioned this can be a strong signal of resolve. Backing down and complying once
sanctions have been initiated is an option but does not come without audience costs.
Since the theory posits that democratic leaders will face greater audience costs for
backing down and admitting a failed public policy, the signal sent by a democratic target
will be more credible than that sent be an autocratic state. Thus, a potential democratic
sender of sanctions that observes this signal of resolve from a democratic target is less
likely to initiate sanctions. If the signal comes from an autocratic state, there is a greater
possibility that the autocratic leader is bluffmg and will back down once sanctions are
implemented. This leads to the ﬁrst dyadic hypothesis: Jointly democratic dyads will
experience fewer episodes of sanctions than other types of dyads.

The essence of the above argument is that policies that detract from the public
good jeopardize a democratic leader’s hold on the reins of power, and that a successful
foreign policy is a public good. Put differently, democratic leaders need to avoid
unsuccessful foreign policies or risk losing political ofﬁce. Nossal (1999) argues that
both of these factors are relevant to an analysis of sanctions. “Sanctions interrupt normal
economic intercourse,” writes Nossal, “and in so doing, they deprive political
communities of the good things necessary to sustain life and community; faced with such

disruptions in the supply of good things, rational governors of a sanctioned community

76

will do whatever is necessary to continue to have access to those good things and will
alter their behavior in such a way as to stop the sanctions” (Nossal: 130).

Nossal also posits a liberal democratic corollary to this argument. If leaders of
sanctioned states do not make the rational choice of changing their policies to escape
sanctions, they will be replaced through the will of those who are governed and are
suffering from the economic effects of sanctions (N ossal, 1999). Kirshner describes the
same expectations. He states that “Pressure on core support groups creates indirect
incentives by motivating those groups to pressure the government to change course, and
by raising fears that dissatisfaction among such groups will cause them to conclude that
their interests can be better served under new leadership” (Kirshner, 1997). Following
this logic, one can see how the motivation for leaders to remain in ofﬁce functions as a
kind of limit on how much economic harm a country will withstand before it changes its
policy to escape sanctions. This is true for the sender of sanctions as well as for the
target. Leaders of a sanctioning country will only be able to maintain a failing sanctions
policy for a limited amount of time before they face the threat of expulsion from office
for their failed policies. Also, leaders who practice poor policies will be less likely to be
in office at any given time.

Since democracies are more dependent than autocracies on the provision of public
goods, I expect that economically harmful sanctions in a democracy will increase the ‘
probability of a shift in support from the current regime to a challenger. As Bueno de
Mesquita et a1 note, challengers in a democracy compete largely over the provision of

public goods; as a result, if sanctions are imposed on a democracy, challengers are likely

77

to rise up. Likewise, democratic leaders whose policies make them continuous targets of
harmful sanctions are also likely to face an increased threat of removal from ofﬁce.

However, this process is signiﬁcantly altered when the governed do not have the
institutions in place for the oversight of ineffective leaders. If the segment of the
population affected by sanctions is not part of a leader's winning coalition for retaining
office, then he/she has no motivation to lift ineffective sanctions or change a policy that is
. attracting sanctions from other countries. In effect, this leads to both longer and more
damaging sanctions when an autocracy is involved. In other words, since autocratic
leaders are generally beholden to a small cadre of elites for their political survival,
sanctions against autocracies may be able to lead to a general decline in the national
welfare with little chance of eliciting the desired policy change. Therefore, as long as
sanctions are not directly affecting the welfare of political elites, autocratic leaders are
expected to be able to withstand the negative welfare effects of sanctions for longer
periods than democracies. This leads to the second dyadic hypothesis: Jointly
democratic dyads will experience shorter and less severe sanctions than other types of
dyads.

In summary, the institutional explanation of economic coercion provides a sound
framework for understanding much of what has previously been regarded as the puzzling
behavior of nations regarding sanctions, and the theory leads to the following monadic
and dyadic hypotheses.

Monadic Hypotheses
H1: Democracies impose sanctions more often than other regime types.

H2: Democracies prefer to impose financial sanctions alone, rather than
combinations that include import or export sanctions. By contrast, autocracies

78

will be less reserved in their sanctions policy, preferring comprehensive
packages of sanctions including both import, and export sanctions.
H3: Democracies are more likely than autocracies to impose minor sanctions.
Dyadic Hypotheses
H1: Jointly democratic dyads will experience fewer episodes of sanctions than other
types of dyads.
H2: Jointly democratic dyads will experience shorter and less severe sanctions than
other types of dyads.
Research Design
I use Hufbauer, Schott, and Elliot’s (Hufbauer, Schott, and Elliot, 1990) sanctions
dataset to analyze all cases of economic sanctions between 1950 and 1990. While this
work has garnered some criticism, most of the critiques have focused on their coding of
success, potential bias toward large states in case selection, and their analysis of the data
(see e.g. Pape, 1997; Drezner, 1999). As a source of sanctions cases throughout the
world, it is the best available.23 The dataset consists of 109 cases of dyadic sanctions.
Some cases in the HSE data set involve multiple senders or targets. As the unit of
analysis is the dyad-year, I disaggregate these into multiple cases. For example, HSE note
that the United States and Great Britain both imposed sanctions against Uganda in 1972.
I code sanctions as occuning for both the-United States-Uganda dyad and the Great
Britain-Uganda dyad in 1972. Similarly, HSE record that the United States instituted
sanctions against both India and Pakistan in 1971. I again code this as two cases of
sanctions, one for the United States-India dyad and one for the United States-Pakistan

dyad. Following Lektzian and Souva (2001), I do not include cases in which the sender

or target is only identiﬁed as an institution or conglomerate of nations. Thus, I do not

79

include the Arab League sanctions against Canada in 1979, nor do I include cases in
which the United Nations is the sole initiator.

To test the ﬁrst hypothesis, that democracies impose sanctions more often than
other regime types, I identify for each case whether the sender is a democracy or not. The
coding of democracy is based on Bueno de Mesquita et al’s discussion of winning
coalition size.24 A state is coded a democracy if it’s winning coalition score is ﬁve.
Table 3.2 provides a preliminary look at the propensity for different polities to use
economic sanctions.

Table 3.2: Initiators of Sanctions By Regime Type, 1950-1990

 

 

 

 

Regime Type Number of Nation Years Number of Nation Years
Without Sanctions With Sanctions

Non-Democracies 5 5 56 1 9

Democracies 1012 90

 

 

 

Chi-Square: 350.95, p < .01

This table provides strong evidence for pursuing the question further. From Table
3.2, l observe that there is a statistically signiﬁcant relationship between regime type and
sanctions initiation. Democracies impose sanctions far more often than one would expect
based on their percentage of the population. While democratic states make up less than

20% of the sample, they impose sanctions more than 80% of the time. Conversely, non-

 

” Drezner (1999) analyzes Russia’s attempts between 1992 and 1997 to coerce states that were once
members of the Soviet Union.

2’ See Bueno de Mesquita and Root (2000: 72-73) for details on how to construct this variable. The
winning coalition measure is composed of four components from the Polity index. A winning coalition
score of 5 indicates that the regime is not a military regime, has competitiveness and Openness in executive
recruitment, and has competitiveness of participation. Winning coalition scores of 5 correlate highly with

Polity democracy index scores of 10.

80

 

democratic states are far less likely to use sanctions than their proportion of the
population would lead one to conclude.

Although the evidence in Table 3.2 is consistent with the hypothesis that
democracies are more likely than other regime types to use economic sanctions, I do not
argue that regime type is the only inﬂuence on the use of sanctions and Table 3.2 does not
include any control variables. I conduct a more thorough analysis of whether democratic

regimes are more likely than non-democratic regimes to initiate sanctions by conducting a

fr‘iqu

regression analysis on all states from 1950 to 1990. This permits the inclusion of other
relevant factors that may inﬂuence the initiation of sanctions. I choose an event count
model over a logistic model as states may choose to initiate sanctions more than once in a
given year.25
The dependent variable in this analysis is the number of times a state initiated
sanctions in a given year. Regime type is the central independent variable of interest.
Democratic states are coded one. In addition to regime type, I control for economic and
political factors that may inﬂuence the use of sanctions. Economic factors that may
inﬂuence the likelihood of imposing sanctions include the wealth and trade dependence
of a state. Sanctions are an effort to use economic force to make another state change a
policy. If a state is very poor, then it is not likely that it will attempt to use sanctions.
The recourse to sanctions depends in part on the potential leverage a state can muster;

thus, I expect wealthier states to use sanctions more often than poor states. Moreover,

wealth may be correlated with democracy, therefore, I control for the wealth of states in

 

2’ 1 also ran a logistic model where a state either initiates or does not initiate sanctions in a given year.

The substantive results are the same: no variables change in sign or signiﬁcance.

81

order to isolate the effect of democracy on initiation. I measure wealth as gross domestic
product per capita, using data from the Penn World Tables (Summers and Heston, 1991).
Trade dependence, though it creates an opportunity to exercise leverage, Should
reduce the willingness of a state to use sanctions. As discussed earlier, sanctions harm,
though not always greatly, a nation’s economy. Perhaps more importantly, the recourse
to sanctions may make ﬁrms in other states less willing to conduct business with ﬁrms in

states that employ sanctions. To protect their own economy and to demonstrate their

I..-

commitrnent to commercial exchange, the more dependent a state is on trade, the less
likely it will be to initiate economic sanctions. I measure this variable with the Penn
World Tables openness variable (Summers and Heston, 1991). Openness is the sum of a
state’s exports and imports divided by gross domestic product.

Next, I expect Major Powers to be more likely to use sanctions than other states.
Major powers not only have broader interests in international events, they also enjoy a
range of options that are not available to lesser powers. Because major powers have the
ability to respond, their inaction sometimes comes at great cost. When a great power
chooses not to respond to events around the world, their inaction can send a signal of
disinterest in the matter, or an inability to respond (potentially seen as a decline in
power), or approval for the actions taken by the foreign country. Thus, the fact that major
powers have the ability to respond is a compelling force to take some action. Major
powers are also more likely to use sanctions because they are more likely to try to impose
their ideology on other states and to try to keep their client states in line. For similar
reasons, the United States has had a greater incentive to use sanctions in the post-World

War 11 period. As the dominant power in the international system, the United States has

82

had an incentive to mold the international system according to its wishes. I use the COW
coding to determine which states are major powers. In the post-World War 11 period, the
COW coding includes the United States, the Soviet Union/Russia, France, Britain, China.

While I expect these other factors to inﬂuence the use of sanctions, I do not expect
them to nullify the importance of political institutions. Indeed, these other factors
establish an appropriate baseline for the use of sanctions. If the arguments are correct, I
should observe that institutional factors exert a statistically signiﬁcant inﬂuence on the
initiation and content of sanctions even in the presence of non-institutional opportunity
.and willingness factors.

Statistically, I employ a general estimating equation (GEE) to analyze the onset of
sanctions. A GEE model permits the analyst to specify a variety of within group
correlation structures. As the data set is similar to Oneal and Russett’s (1997, 1999, see
also Russett and Oneal, 2001) analyses of militarized disputes, I follow their lead and
specify an AR(l) correlation structure.26 Zorn notes that population-averaged models,
such as GEE, are “valuable “for making comparisons across groups or subpopulations”
(Zorn, 2001: 475). Since the substantive focus of this research is on the general
likelihood of a state initiating sanctions, a population-averaged approach is an appropriate
statistical method.

The multivariate analysis of the initiation of sanctions indicates that democracy is
a statistically signiﬁcant and positive inﬂuence (see Table 3.3). This supports the

hypothesis (monadic hypothesis 1) that democracies are more likely than other regime

 

2" I also ran the analyses using alternative correlations structures, such as AR(2) and AR(3). The

results are the same.

83

types to impose sanctions. Substantively, the inﬂuence of democracy is great. Changing
a state from a non-democracy to a democracy, holding all other variables constant,
increases the probability that a state will initiate sanctions by 184% (see Table 3.3,
Column 3)!

Table 3.3: Determinants of State Initiation of Sanctions, 1950-1990

 

 

 

 

 

 

 

 

 

Variable Model 1: Monadic First Differences '
Analysis
B
s.e.
Democratic Initiator 1.044 *** + 184%
.333
Monadic Trade -.030 *** -70.85%
Dependence/Openness .012
GDP per capita ' .0002 *** + 89.39%
.00003
Major Power 2.214 *"‘*
.385
USA .763 *
.426
Constant -5.391 *"
.520
N 4228
Wald 245.60 ***
*** p < .01
** p < .05
p-values reﬂect one-tail
tests.

 

 

 

' The ﬁrst differences reﬂect changes in the predicted probability of event occurrence
relative to a baseline model where democracy, USA, and major power are set equal to
zero and openness and GDP per capita are set at their mean values.

The economic control variables also operate as expected. Greater trade
dependence is statistically signiﬁcant and reduces the likelihood that a state will initiate
sanctions. The effect of economic openness is substantively meaningful. Increasing a

state’s economic openness by one standard deviation decreases the likelihood of sanctions

by 70% (Table 3.3, Column 3). Consistent with the argument that as the vulnerability of

84

 

a state decreases it is more likely to impose sanctions than other states, I observe that
states with higher gross domestic product per capita are more likely to impose sanctions.

The opportunity control variables, major power status and the United States
dummy variable, also function as expected. Major powers are more likely than other
nations to impose sanctions, and the United States, in particular, is more likely than other
states to initiate sanctions. Perhaps what is most important about these variables is the
. fact that the other more theoretically meaningful variables are still statistically signiﬁcant
and operate as hypothesized even in the presence of these statistical controls.

The second hypothesis concerns the type of sanctions that will be imposed, trade
sanctions, ﬁnancial sanctions, or some combination of the two. For this analysis, I
classify the type of sanctions (export, import, or ﬁnancial) implemented for each
sanctions case using the Hufbauer, Schott, and Elliot data. I argue that, while
democracies are willing to use trade sanctions, they are more likely to use ﬁnancial
sanctions because ﬁnancial sanctions cause less damage to the sender’s economy. Table
3.4 reveals that even though democracies use sanctions more often than non-democracies,
regime type also inﬂuences the type of sanctions imposed.

Table 3.4: Type of Sanctions by Regime Type

 

 

 

 

 

 

 

 

Regime Type Trade Sanctions Financial Sanctions Trade and Financial
Only Only Sanctions

Non-Democracies 5 0 7

Democracies 12 28 35

 

Chi-square: 8.26, p < .05

In particular, democracies use ﬁnancial sanctions more often than one would

expect; in fact, non-democracies never use ﬁnancial sanctions alone in the sample. On

the other hand, about 56% percent of the time a non-democracy uses sanctions, it imposes

 

comprehensive ﬁnancial and trade restrictions, whereas democracies resort to
comprehensive sanctions packages only about 46% of the time. Overall, Table 3.4 is
consistent with the expectations regarding the type of sanctions a particular regime is
likely to implement.

Next, I conduct a test of monadic hypothesis three: democracies are more likely
than autocracies to impose minor sanctions. To assess this argument, I recode HSE’S ﬁve
sanctions goals into a dichotomous indicator of major and minor goals. This
dichotomous recoding is based on the arguments ofDrezner (1999) and Hart (2000).
Table 3.5 shows that there is a statistically signiﬁcant difference in the type of goals
democracies and non-democracies pursue.

Table 3.5”: Goal of Sanctions by Regime Type

 

 

 

 

 

 

Regime Type Minor Goal Major Goal
Non Democracy 4 14
Democracy 48 38

 

 

Chi-Square = 6.72, p < .05

Democracies are signiﬁcantly more likely to pursue a minor sanctions goal than
are non-democracies, even controlling for the greater use of sanctions by democratic
states. This difference between democracies and non-democracies regarding the type of
sanctions goal pursued is consistent with the institutional argument. The institutional
incentives to promote the public welfare encourage democracies to pursue goals that are
less likely to damage their own welfare.

One of the most important and robust empirical trends in international relations is

that democracies do not enter into war against other democracies. Based on a similar

86

 

.1... It; 171

causal logic, I posit that jointly democratic sanctions, that is when both the sender and

target are democracies, are less likely to occur than sanctions in either mixed or jointly

autocratic dyads. Table 3.6 shows that there is a statistically signiﬁcant difference in the

onset of sanctions between jointly democratic and other types of dyads.

Table 3.6: Sanctions by Dyadic Regime Type

 

 

 

 

Dyadic Regime Type Number of Cases Without Number of Cases With
Sanctions Sanctions

Dyads with at least one non- 31229 103

democracy

Joint Democratic Dyad 3919 5

 

 

 

Chi-square: 4.63, p<.05

Democratic dyads experience fewer cases of sanctions than their proportion of the

population would lead one to expect. Given that the cost of sanctions is not as great as

the cost of war, I do not expect the sanctions relationship to be as robust as its military

force analog. Indeed, there are ﬁve cases of democracy versus democracy sanctions in

the dataset. However, democracies are frequent users of sanctions, having sanctioned

non-democracies 85 times.

The analysis in Table 3.6 does not control for other relevant inﬂuences on the use

of sanctions. To better determine whether joint democracy reduces the likelihood of

sanctions, I conduct a multivariate analysis on the determinants of economic sanctions for

all politically relevant dyads between 1950 and 1989. Whereas the previous analysis in

Model 1 (Table 3.3) focused on the monadic determinants of sanctions initiation, this

analysis emphasizes the dyadic inﬂuences of sanctions onset. To the best of my

knowledge, this is the ﬁrst test of this type with regard to economic sanctions.

 

27 There is a slight difference in the number of cases between Table 4 and 5 do to incomplete

87

 

As before, the dependent variable is whether or not sanctions were initiated in a
particular dyad-year. Similarly, the substantive focus of this analysis is on the general
likelihood of a dyad experiencing sanctions; therefore, I employ a general estimating
equation, which assesses population averages, with an AR(l) correlation speciﬁcation.

In terms of independent variables, I posit that many of the same factors that
inﬂuence the decision to initiate war also inﬂuence the decision to impose sanctions on
another state. First, I contend that sanctions will be less likely to occur in jointly
democratic dyads then they will be in other types of dyads. I code a dyad as democratic if
both states have winning coalition scores of four or ﬁve.

I also believe that many “realist” factors inﬂuence the onset of sanctions. Based
on the insights of power transition theory, I contend that as power becomes more equal in
a dyad, the likelihood of sanctions occurring increases. Power parity creates a greater
opportunity for sanctions. In dyads with a highly asymmetric power distribution, the
weaker state has little reason to believe that it can prevail in a crisis bargaining situation
so it is more likely to yield to the demands of the stronger state. I measure relative
capabilities using the COW composite capabilities index, taking the natural log of the
ratio of the stronger state to the weaker one.

Next, I expect that foreign policy preference Similarity reduces the likelihood of
imposing sanctions. States with similar preferences have less to ﬁght over, thus sanctions
are less likely to occur. To capture this effect, I code whether or not two states are allies.

Although allied states do not have identical preferences, research has shown that they are

 

information on sanctions type.

88

less likely to have militarized conﬂict, an effect that is particularly strong in the post-
World War II era (Russett and Oneal, 2001).

I also anticipate that political leaders prefer to sanction states with whom they are
less economically dependent as this will likely involve lower costs to the home, i.e.
sanctioning, state. In other words, a state does not want to sanction a state that it
conducts a lot of trade with as this state then has the ability to inﬂict a similar amount of
damage on it. Thus, as trade interdependence increases, the likelihood of sanctions
decreases. For similar reasons, the greater the distance between two states, the more
likely sanctions are to occur. I measure interdependence as exports plus imports divided
by gross domestic product. I measure distance as the great circle distance between each
state’s capital.28

Finally, a signiﬁcant number of the sanctions episodes in the HSE data set involve
the United States. Whether this is the result of a systematic bias or forces peculiar to the
United States that impel it to use sanctions is unclear. To err on the side of caution, I
include a dummy variable that takes on a value of one if the United States is a member of

the dyad.

 

2’ Trade interdependence data comes from Oneal and Russet, and 1 use EUGene (Bonnet and Stam,

2000; v. 2.4) to calculate state-to-state distance.

89

Results

The empirical results in Model 2 (Table 3.7) support my primary hypothesis.

Table 3.7: Determinants of the Onset of Dyadic Sanctions, 1950-1990

 

 

 

 

 

 

 

 

 

 

Variable Model 2: Dyadic Analysis First Differences '
B
s.e.
Joint Democracy -0.707 ** -50.69%
0.337
Dyadic Trade Dependence, -211.155 ** -77.41%
weak link 88.016
Ln Relative Capabilities -0.515 *** -59.61%
0.091
Allies 0.672 **
0.333
Ln Distance 0.094
0.089
USA 3.724 ***
0.542
Constant -6.382 ***
0.490
N 26514
Wald 136.83 ***
an p < .01
** p < .05

p-values reﬂect one-tail
tests.

 

 

 

” The ﬁrst differences reﬂect changes in the predicted probability of event occurrence relative to a baseline
model where democracy, USA, and major power are set equal to zero and openness and GDP per capita are

set at their mean values.

Jointly democratic dyads are less likely to experience sanctions than other types of dyads.

The pacifying effects of democratic institutions, then, extend beyond the military realm

and into the economic. Moreover, this analysis provides additional support for the idea

that democracies have less to ﬁght over. In other words, democracies have fewer

militarized disputes because they have fewer non-militarized disputes.

90

 

I also ﬁnd that economic interdependence reduces the likelihood of sanctions.

, States are more likely to sanction a nation with whom they have low levels of trade than a
nation with whom they have extensive trade ties. Given that sanctions are a two-edged
sword, this is not surprising. Sanctions necessarily entail losses for the sanctioning state,
as well as the target state; thus, a state motivated by welfare desires will prefer to sanction
a state with whom it does not have much trade. I

In addition to the effects of democratic institutions and economic
interdependence, I ﬁnd little support for ‘realist” inﬂuences on the occurrence of
sanctions. Contrary to expectations, the presence of an alliance increases the likelihood
of economic sanctions occurring. An analysis of the data suggests that this is largely due
to the United States sanctioning a number of Latin American countries. Indeed 22 of the
31 cases in which an allied is sanctioned are cases in which the United States is
sanctioning a Latin American nation.

One of the most robust results in research examining the onset of rrrilitarized
disputes concerns the pacifying effects of power preponderance. I ﬁnd that an
asymmetric power distribution also reduces the likelihood of sanctions. To some extent
this effect is mitigated by the statistically signiﬁcant and positive inﬂuence of the United
States dummy variable. Not surprisingly, dyads that involve the United States are more
likely to experience sanctions than other dyads. Since most of these dyads have an
asymmetric power distribution, the pacifying effects of power preponderance appear to be
limited to non-United States dyads.

Overall, the multivariate analysis not only supports the argument that democratic

dyads are less likely than other types of dyads to experience sanctions, the analysis rules

91

 

out competing explanations. For instance, the institutional theory improves on
substitutability arguments. A general substitutability argument might suggest that states
substitute sanctions for military force in Situations where the cost of using force would be
too high. By including a measure of the balance of power in a dyad I am able to address
this issue. If the use of sanctions is primarily a substitute for military force, then I am
unlikely to observe a statistically signiﬁcant difference across dyadic regime types,
especially while controlling for the power relationship in a dyad. This is not to suggest
that sanctions are never used as a substitute for military force. In some situations
sanctions may be substitutes for force, though they are more likely to accompany the use
of force. More importantly, the empirical analyses suggest that internal political
dynamics exert a far greater inﬂuence on the use of sanctions than external balance of
power politics.

Finally, I present a preliminary and limited test of dyadic hypothesis two by
examining the mean length of sanctions across dyad type. Although democracies may
occasionally sanction other democracies, I expect the mean length of jointly democratic
sanctions to be shorter than the mean length in other dyad types. As mentioned earlier,
the importance democracies attach to the public welfare makes them more likely to
accede to sanctions demands. Autocracies, however, face a smaller incentive to give in to
sanctions demands, so I expect that sanctions against an autocracy, whether imposed by a
democracy or not, will be longer. Table 3.8 indicates that the mean length of sanctions in
jointly democratic dyads is almost half the length of sanctions in jointly autocratic dyads.
Consistent with the hypothesis, jointly democratic dyads also experience shorter sanctions

than mixed dyads.

92

Table 3.8: Length of Sanctions by Dyad Type

 

 

 

 

 

 

Dyad Type Mean Length of Sanctions in years
Jointly Democratic 5.0
Mixed Dyads 6.42
Jointly Autocratic 9.8

 

Conclusions Regarding the Initiation of Sanctions

The thesis of this paper is that the inﬂuence of democratic institutions in
international relations extends beyond the military realm and into the economic. I
broaden Bueno de Mesquita, Morrow, Siverson, and Smith’s (1999) institutional theory
of military conﬂict to explain the use of economic sanctions in world politics. I ﬁnd that
democracies are more likely to initiate sanctions than other regime types, even while
controlling for other relevant inﬂuences. Nonetheless, democracies are less likely to
impose sanctions against a democracy than they are to impose sanctions on a non-
democracy. The pacifying inﬂuence of jointly democratic regimes, then, extends into the
economic domain. This theory generates additional foreign policy expectations. When
democracies do impose sanctions, they are more likely than autocracies to utilize
ﬁnancial over trade sanctions and aim for minor foreign policy goals. Both of these
hypotheses are conﬁrmed by the empirical record.

The essence of the theory is that political institutions affect the incentives of
leaders, and therefore, the foreign policies of states. Democratic leaders are expected to
be more sensitive to hardships placed on the general population. However, this
expectation is not reached based on an assumption of greater morality among leaders of

democratic nations. All leaders are assumed to be acting on their own selﬁsh interests to

93

 

remain in ofﬁce. It is the institutional arrangements that provide the incentive for
democratic leaders to be responsive to economic losses brought about by sanctions
(Bueno de Mesquita et al., 1999; p. 793). The work of Immanuel Kant provides the
philosophical grounding for this idea.

A problem like this [perpetual peace] must be capable of solution; it does not
require that we know how to attain the moral improvement of men but only
that we should know the mechanism of nature in order to use it on men.
We can see, even in actual states, which are far from perfectly organized, that
in their foreign relations they approach that which the idea of right prescribes.
This is so in Spite of the fact that the intrinsic element of morality is certainly
not the cause of it. (A good constitutions in not to be expected from morality,
but, conversely, a good moral condition of a people is to be expected only
under a good constitution.) Instead of genuine morality, the mechanism of
nature brings it to pass through selﬁsh inclinations, which naturally conﬂict
outwardly but which can be used by reason as a means for its own end, the
sovereignty of law, and, as concerns the state, for promoting and securing
internal and external peace (1957: 30-31).

The 'mechanism of nature' for assuring the morality of men that Kant speaks of is, in

international relations, the presence of democratic institutions.

94

Chapter 4:
Threats, Success, and United States Drug Sanctions Policy

In the remainder of the dissertation the emphasis is shifted from factors affecting

the initiation of sanctions to factors affecting their success. However, this is not a typical

treatment of sanctions success. Typically, researchers examine cases of sanctions that

have occurred and attempt to determine why some of them have been successful while

others have not. Most of these studies concluded that sanctions are an ineffective policy

instrument because of the low probability of coercing a target nation to change its policy
once sanctions have been initiated (Preeg, 1999; Renwick 1981; Strack, 1978; Knorr,
1975). For the most part, this conclusion has been reached based on a simple observation
of the percentage of observed sanctions cases that achieve their stated policy goal. Since
even the most optimistic of estimates has sanctions succeeding only about 35% of the
time (Hufbauer, Schott, and Elliot, 1990), many of these researchers have been puzzled
by why states would choose sanctions.

In this work a different approach will be taken to solving the puzzle of why
sanctions are so frequently used even though their success rate is generally considered to
be low. The central part of the argument made here is that observed cases of sanctions

should have a much lower rate of success than threatened sanctions. Most cases of
successful sanctions may be occurring at the threat stage, never making it into typical
empirical analyses of sanctions. If this is true, then estimates of sanctions success based

solely on observed cases of sanctions will suffer from selection bias that prejudices

indings toward failure.

95

 

Whenever sample selection is based on certain criteria there is the risk of selection
bias occurring. In other words, the results of the study could be biased by the selection of
cases being examined. “The logic is that if the sample is not representative of the larger
population, then even if the inferences are valid for the sample, they may not be for the
larger population” (Signorino, 2001; p. 5). Without accounting for this selection bias, one
must assume that the cases under examination are not signiﬁcantly different from the
entire sample, which we cannot observe. However as pointed out above, and shown
formally below, there are strong reasons to believe that this assumption is untenable. In
other words, if some countries are giving in to the mere threat of sanctions, then there are
good reasons to believe that the group that does not give in at the threat stage may be
substantively different from the general population of states.

One way to determine if those cases being studied (i.e. cases where sanctions
actually occur) are in fact different from the general population of potential sanctions
cases is to use a uniﬁed model that looks at factors affecting the onset of sanctions and
factors affecting the success of sanctions (see Lemke and Reed 2001 for an application of
strategic censoring to rivalry studies, see also Reed, 2000). If the two events are
signiﬁcantly correlated, then one can conclude that whether sanctions are imposed is due
in part on the likelihood of success in sanctioning.

The application of the uniﬁed model to sanctions can be thought of as Shown in
the following ﬁgure, where yl and y2 are two separate dependent variables showing when
sanctions occur and when they are successful, respectfully. In this diagram, the analysis
starts with all dyads. Moving to the right, Y1 indicates which of those potential dyads

chose sanctions. Y2 measures which of those observed sanctions cases were successful.

96

Figure 4.1: Selection Diagram

m
"U

co

>»
G
<1:

 

Given that estimates of models of this type (Nooruddin, 2002; Lektzian and Souva, in
progress) have found that factors affecting the success of sanctions are inﬂuencing the
decision to implement sanctions, a closer inspection of this relationship is warranted. If
this selection process is taking place, then one has to be concerned with the process
through which it occurs.

By a closer inspection, I am primarily interested in why some cases do not make it
into the observed cases of sanctions. A typical simpliﬁed selection model would look

something like this:

Figure 4.2: Basic Selection Model of Sanctions

 

Dyads Failed Sanctions
With

Sanctions

S \
All / C Successful Sanctions

Dyads

 

 

 

 

 

 

97

 

But there is more than one way that a case can make it into the non sanctions (~S)
branch of this model. First, there may have been no dispute between the two countries in
the ﬁrst place, which is probably the least interesting but also probably makes up the

majority of cases that fall under the ~S branch. For instance, in all observed years,
Armenia and Bougainville fall into the ~S branch of this diagram because they simply
:I

had no reason to even considering sanctioning one another. There are at least three other

I .

ways that a dyad could fall into the ~S branch that are theoretically more interesting

because they start by assuming that states are having a dispute and sanctions are

considered but never carried out. First, the potential threatener of sanctions could be

deterred from making a threat. Second, sanctions could be threatened with the

threatening state backing down and last; the threatened state could back down before

sanctions are actually implemented.

Thus, it is at this threat stage of sanctions where potential selection problems
arise. If the selection process is at work, states that expect to be sanctioned successfully
may be more likely to simply give in to the demands of the sender at this stage. Having

gotten its way, the sender no longer needs to impose sanctions and history never records a

sanction having taken place. On the other hand, a target that stands ﬁrm at this stage may
be signaling to the sender nation that it will not be coerced into changing its behavior

through economic sanctions. In this case, the sender may decide that it will be

unsuccessful in using sanctions and back down from its threat. Again, history records no

136 of sanctions, although a failed attempt at using economic force to coerce political

ehavior has certainly occurred.

98

 

The frnding of the two selection modesl performed on this question to date
(Noorudin, 2002; Lektzian and Souva, in progress) suggest that the ﬁrst part of this
explanation may be occurring with greater frequency than the second part. Targets are
selecting themselves out of potential sanctions episodes where they expect to fail. In
other words, sanctions that would be successful are less likely to occur. However,
senders are not selecting themselves out with as great of a frequency when they expect to

be unsuccessful. Thus, the subset of sanctions cases that are observed are those that are

most likely to fail. The expected impact on empirical studies is that estimates of

sanctions success will be biased downward.

While analyzing the process with a selection model is not without merits, it also
presents some conceptual problems. First, by looking at dyadic outcomes like,
sanctions/no sanctions or success/failure, it misses the strategic nature of decisions by the
potential sender and target of sanctions. Second, it does not explicitly address threats of
sanctions. It shows if sanction initiations and successful sanctions are correlated, but it
does not tell us why sanctions that are likely to be successful do not occur. (i.e., it does

not help identify whether the observed selection bias is a result of senders not threatening
when they expect to be unsuccessful, or threatening and backing down, or targets backing
down after threatened).

Thus, a more direct way to examine the process involved in the selection of the

subset of cases that become sanctions is to modify the selection model presented above in

two ways. First, I include strategic decision-making, and second, I focus on both the
factors affecting the success and failure of threats of sanctions and factors affecting the

:uccess and failure of observed sanctions cases. By doing this we can learn more than

99

 

 

 

from the typical selection model which has told us that factors affecting success are
negatively related to the imposition of sanctions.

With perfect information, if sanctions were going to be successful after they were
implemented, we would expect targets to always give in at the threat stage of sanctions.
By doing this targets avoid the negative effects of being sanctioned and then having to
give in at a later point. Logically, if the target knows that it cannot withstand the costs of
sanctions it might as well give in without sanctions and avoid the economic loss.

Figure 4.3: Threats of Sanctions With Complete Information

 

 

 

 

 

Sen Threat ® Resist Sen Sanction Tar Resist
~Threat Com I ~Sanction Failed
p y Comply Sanctions
Status Successful Failed Successful
Quo Threat Threat Sanctions

In Figure 4.3, the basic selection model presented earlier is expanded to include
strategic behavior by potential senders and targets of sanctions as well as adding threats
of sanctions. By extending the basic selection effects model back two stages, I have
identiﬁed more precisely what I mean by Not Sanction (~S). Now instead of ~S being
dyads without sanctions, it is identiﬁed as cases of failed threats of sanctions. And, in the
preceding branch, cases of successful threats are identiﬁed. This allows us to look more
closely at why the sanction did not occur. It could have not occurred because the Sender

was able to achieve its goal with the mere threat of economic sanctions, or it might have

100

not occurred because the Sender was deterred from carrying out its threat of sanctions.
Going back one step further, reveals that the sender may have even been deterred from
making a threat in the ﬁrst place”.

In the next section of this chapter on threats of sanctions, I will present a

theoretical explanation of how differing domestic institutions in senders and targets of

sanctions affect the likelihood of sanctions being threatened or implemented. Following

that I will present an explanation of the United States sanctions policy against countries
that are deemed not to be cooperating in the ﬁght against international drug trafﬁcking.
In the future I will frequently refer to this policy simply as the United States drug
sanctions. This information is presented prior to the speciﬁcation of the formal model
because the formal model, while general in form, is designed with the intent of testing it
on data from the United States drug sanctions. Following the brief explanation of this
policy, a formal theoretical model of threats of sanctions is presented. Theoretical and

testable hypotheses are derived from this model, which are operationalized and tested in
the next section with data from the United States drug sanctions.

Threats of Sanctions and Domestic Institutions

In the previous section I introduced the logical groundwork for incorporating
threats into a theoretical model of sanctions. Extending the extensive form game tree

back two stages, also helps us in formulating theories about why events end up in the

‘Not Sanction’ or the ‘Not Threat’ branch of the game.

 

‘9 This logic could be inﬁnitely regressed backward. For example the sender might not have
threatened because the target capitulated before the threat was necessary. At this point though, there must

have been an implicit threat of sanctions that caused the target to give in. Otherwise, it would not be
considered in the realm of this or other research on sanctions.

101

 

ﬁfx

 

Bueno de Mesquita (2000) refers to the form of punishment associated with the
threat of sanctions as “anticipatory punishment.” According to Bueno de Mesquita,
“. . .costs associated with credible threats are generally smaller than those that accrue
when a punishment is actually meted out. Creating the anticipation of punishment may
avoid signiﬁcant costs while still being effective” (Bueno de Mesquita, 2000; p.178).
Therefore, in terms of economic sanctions, if the exercise of power to coerce a nation to
change its behavior can be accomplished with the mere threat of a break in trade, then
economic power has been used to coerce political behavior and an argument can be made
that a sanction has occurred and succeeded. In fact, successful threats represent a more
unqualiﬁed success than the successful implementation of sanctions because the goal is
achieved at a smaller cost.

Therefore, potential senders of sanctions should have frequent occasion to
consider bluffrng, since successfully blufﬁng allows the sender to achieve its goals at very
little cost. However, since potential targets know that senders have an incentive to bluff,
those targets have an incentive to occasionally challenge threats — even when they,
themselves, are blufﬁng and would be forced to back down if challenged.

The problem for states with bluffmg is that they may be called on their bluff and
being called on a bluff and having to back down does not come without costs. These
costs are generally referred to as audience costs and are thought to occur at varying
degrees depending on the type of domestic political institutions in a state. “In general,
leaders of democratic states are most vulnerable to domestic fallout from failed foreign

policies and autocrats are least vulnerable. This-is true because democratic leaders are

102

 

 

more likely to be thrown out of office when their policies fail than are autocrats or
dictators” (Bueno de Mesquita, 2000; p.181).

Considering these differing rates of audience costs in conjunction with the
theoretical perspective developed in chapter 2 of this dissertation regarding the
importance of domestic institutions in economic sanctions, I would propose that sanctions
are best conceptualized as a type of signaling game. After the sender threatens sanctions,
. the target can either comply with the sender’s demands and avoid the sanctions
(Successful Threat), or it can send some signal about its ability to resist if the sender
imposes sanctions. If the target’s signal is compelling enough, the sender will back down
without imposing sanctions. If the signal is not believable, the sender will call the
target’s bluff by imposing sanctions. As in previous work on the credibility of signals in
international relations (Bueno de Mesquita, 2000; p.181), I propose that countries with
democratic regimes will face higher costs for backing down once they have made a
commitment to a policy. Therefore, they will be less likely to back down after sanctions
have been initiated, and will in effect give a more credible signal of their intentions.

Against a democracy, assuming that its threat to resist imposed sanctions is
credible, one would expect sanctions to be successful at the threat stage or not at all.
Against an autocracy that faces fewer costs for blufﬁng and backing down, sanctions may
be less successful at the threat stage (because autocracies will be more likely to resist by
blufﬁng) but more likely to succeed when sanctions are actually implemented (because
bluffmg autocracies can back down at less cost to them). Given this logic, democracies
should be much less likely to bluff. Therefore, all else equal, sanctions should have a

tendency to be more successful against democracies at the threat stage but should be very

103

unlikely to be successful once implemented. As expected in the literature on military
conﬂict, democracies should choose their ﬁghts very careﬁrlly and should be highly likely
to be successful when they choose to make a stand.

It is important to note that this logic is contrary to the expectations and ﬁndings of
other studies on the effect of democracy on sanctions success. Other studies have
concluded that sanctions that are implemented should be more successful against
democracies than autocracies because, among other things, democracies will have a
harder time withstanding the economic costs of sanctions. See Bolks and Al-Sowayel
(2000), and Nossal (1999) for these arguments. However, both of these studies have
methodological issues that make their conclusions less than certain. For example, Nossal
only looks at cases of sanctions success and then shows that most of the successes were
against liberal democracies. He says nothing about the regime types in cases of failed
sanctions, which makes this less of a test and more of a descriptive narrative. Bolks and
Al-Sowayel, on the other hand, include both success and failures, but deﬁne success
based on the duration of the sanction, which seems less than intuitive. In fact, looking at
111 disaggregated sanctions episodes since 1948, taken ﬁ'om Hufbauer, Schott, and
Elliot’s data, shows that sanctions are just about as likely to be successful against
autocratic targets as democratic targets. Sanctions were successful 15 out of 31 times
against democracies and 39 out of 80 times against autocracies. Although the objective
of this paper is not to test its hypotheses against the Hufbauer, Schott, and Elliot data,
there is some evidence found in that data set to support the idea that democracies are less
likely to back down once they have made a foreign policy decision. Of the 15 successes

against democracies 66% occurred within the ﬁrst year. Whereas 75% of the success

104

against non-democracies occurred after the ﬁrst year. It appears that democracies that
bluff are likely to back down very quickly, before audience costs have built substantially,
while autocracies might not be as sensitive to audience costs, making them more likely to
back down even after the dispute has been going on for some time.

In the general chapters that were presented earlier it was shown that democracies
are less likely to sanction other democracies than they are to sanction autocracies. This
research asks whether this ﬁnding may also be due to selection effects. In the theoretical
portion of this paper, it is proposed that democracies may be more likely to comply with
the demands of other democracies at the threat stage, thus leaving less actual cases of
sanctions implemented against democracies.

An additional hypothesis that follows directly from this signaling argument is that
we should see more sanctions actually imposed against autocracies since they will send
noisier signals than democracies. Also, since democracies are more likely to be sensitive
to economic losses brought about by sanctions, they are more likely to give in at the threat
stage. Democracies will not be likely to bluff about their ability to withstand these losses
since they face greater audience costs for blufﬁng and backing down. On the other hand,
autocracies are less likely to be hurt by the additional losses imposed on them by
economic sanctions. Therefore, in the data set of observed sanctions we should see many
more actual impositions of sanctions against autocracies than against democracies. In
the chapter of this dissertation on the initiation of sanctions, that expectation was borne

out, although in that chapter a more traditional theoretical explanation was offered for the

ﬁnding.

105

Since targets of sanctions will have to face additional costs for blufﬁng and backing
down, one would expect sanctions to be successful at the threat stage or not at all. This is
particularly true for sanctions imposed against democracies since they pay such a high
cost for threatening and then backing down. The above discussion leads to the following
three hypotheses about threat behavior.

H1: Sanctions that are threatened against democracies should be more likely to be
successful than those threatened against other regime types.

H2: Sanctions that are imposed against democracies should be less likely to be
successful than those imposed against other regime types.

H3: In general, senders should be more successful at using economic force to coerce

targets to cooperate at the threat stage than at the implementation stage.

The Observation of Threats of Sanctions: United States Policy Toward
International Narcotics Trafﬁcking

The United States’ sanctions policy against countries that trafﬁc in illegal
narcotics, which went into place operationally in 1988, is ideally suited for this an
analysis of threats of sanctions because the structure of the policy has a legislated and
observable threat stage. During the life of the policy, there have been very few instances
where sanctions have been imposed and the target country has responded by being more
cooperative with the United States’ goals regarding the trafﬁcking of illicit narcotics.
However, in many cases, after the United States threatens a country with sanctions, the

target takes actions that result in no actual sanctions being imposed.

106

Thus, in this policy, we have the unique opportunity to observe threat behavior in
the area of economic sanctions. This is particularly interesting for sanctions because, as
discussed in previous sections, there are sound reasons to expect that much of the success
of sanctions will take place at the threat level and not the implementation stage.
Furthermore, if this is true, empirical studies focusing on implemented sanctions may be
entirely missing the biggest portion of sanctions successes.

Putting this one single policy under a microscope also has distinct advantages for
the empirical analysis to follow. To analyze threats and uses of sanctions within a uniﬁed
(theoretical-empirical) model, threats of sanctions must be operationalized. Naturally it is
a difﬁcult task to record events that did not occur. Thus, the observation of sanctions that
were threatened but were never implemented poses a difﬁculty for empirical analysts that
has yet to be overcome. Since this policy is far reaching in scope, affecting more than 30
countries over its 15 year history (See appendix 1 for a list of countries affected), and
explicit in when a threat has been made and when the threat was or was not carried out, it
is ideally suited for empirical analysis.

There are also good reasons to expect that any losses in generality associated with
this smaller set of observations will be minimal and the gains from including threats will
greatly outweigh those losses. First, in the data set used for virtually all empirical studies
of sanctions, (Hufbauer, Schott, and Elliot, 1990) over 70 percent of the sanction cases
are initiated by the United States, and an even greater percent is comprised of countries
that would be similar to the United States on country speciﬁc indicators used in most
empirical analysis (i.e., they are large economically industrialized democracies).

Therefore, it is not unreasonable to assume that an explanation of United States sanctions

107

behavior is similar to an explanation of all countries that use economic sanctions.

Second, almost every major empirical study of economic sanctions has relied on the
Hufbauer et. al. (1990a 1990b) data set. Introducing new data sets of sanctions cases, like
the one developed here, or Drezner’s (1999) analysis of Russian coercion attempts among
the newly independent states, is a valuable contribution to the sanctions literature. Third,
as Drezner (1999) notes, large sample studies of economic sanctions such as the Hufbauer
et. a1. study have the problem of comparing demands and concessions across events.
Drezner states “It is difﬁcult to contrast the signiﬁcance of El Salvador’s 1987 concession
to the United States to improve its human rights regime with the import of Israel’s 1982
agreement to halt its invasion of Lebanon” (Drezner 2001: 222). By focusing exclusively
on the United States drug sanctions policy, the type of demand, and the type of sanctions
threatened remain constant across the data (although the perceived level of demand varied
by target state). The ﬁnal advantage of analyzing this one sanctions policy by the United
States is clear since it has an explicit threat stage incorporated into it that allows the
identiﬁcation and analysis of threats versus implementations of sanctions.

Part of what makes the United States’ efforts to combat drug trafficking so
appropriate for this analysis is the legislation passed by Congress, which governs United
States actions in this area. In 1986, the United States Congress amended the Foreign
Assistance Act (FAA) of 1961 under sections 489-490 in order to better address the issue
of international narcotics trafficking. Under this amendment the president is required to
submit an annual report to Congress listing all of the major illicit drug producing and
transit countries. As the ﬁrst step in a two-stage process (See Figure 4.4), this list must

be submitted to Congress no later than November lst of each year. Once a country is

108

placed on this list, also known as the “Majors” list, half of most United States
government foreign assistance is temporarily withheld from listed countries until the
president determines whether each of the countries on the list should be “certiﬁed” as
cooperating in the ﬁght against drugs. With half of their aid temporarily suspended, and
the rest hanging in the balance, foreign countries can be sure that the United States is
serious in its demands for cooperation. Thus, when a country ﬁnds itself placed on the
list of majors, it faces a clear threat of economic sanctions if it does not take certain
actions demanded by the United States.

Figure 4.4: US Drug Sanctions Process

 

US: Stage 1 -

 

 

 

 

 
   
   

 

Submission of Majors
Country Is Not Named: Country ls Named:
Sanctions Are Not Threatened. Sanctions Are Threatened.
Status Quo Remains 1/2 of All US Foreign Aid
Temporarily Withheld
Refuse to
Cooperate with US Target
US: Stage 2 - Cooperate with US
Certiﬁcations Dem?!“ N0
Sanctions Imposed,

 

 

 

De-oertiﬁcation with Country Gets All of

  

Imposition of Sanctions: Its US Aid

De-certiﬁcation with
National Interests Waiver:
No Sanctions lrnposed -
US Backs Down

Cooperate with Cooperate with US

US Demands: Demands: Sanctions

Sanctions Are Lifted After (he

Continue for “W

Another Year

109

Stage two of this process, submission of the president’s certiﬁcation decisions to
Congress, must be completed by March lst of each year. The two-stage process, with
four months between the stage one threat and the stage two certiﬁcations, makes this
legislation ideally suited for the study of the response of nations to the threat of sanctions.
During the period between being listed and being certiﬁed, countries have an opportunity
to respond to the demands of the United States and take positive steps to signify their
. cooperation with the United States in combating the production and trafﬁcking of
narcotics in and through their country. This is what has occurred in several instances,
with reports of stepped up border controls, the widespread burning of drug crops, and the
implementation of new task forces and policy initiatives during this period.

It is important to understand that being included on the list of named countries
does not necessarily mean that a country supports the transit of illicit narcotics. Being
named only means that there is a large quantity of illicit narcotics produced in or
transiting through the country and the United States is concerned that the county is not
doing all it can to prevent this. As deﬁned by the United States Department of State in
the “Legislative Basis for the INCSR” a major illicit drug producing country is one in
which:

A. 1,000 hectares or more of illicit opium poppy is cultivated or harvested during a
year;

B. 1,000 hectares or more of illicit coca is cultivated or harvested during a year; or
C. 5,000 hectares or more of illicit cannabis is cultivated or harvested during a year,
unless the president determines that such illicit cannabis production does not

signiﬁcantly affect the United States. FAA § 481(c)(2).

And a major illicit drug-transit country is one:

110

A. that is a signiﬁcant direct source of illicit narcotic or psychotropic drugs or other
controlled substances signiﬁcantly affecting the United States; or

B. through which are transported such drugs or substances. FAA § 481(c)(5).

While the above deﬁnitions are fairly explicit, the decision of the United States to
include a country on the list of majors is not as straightforward as it might seem. Many
countries are major thoroughfares (U .K., Germany, Italy) or end points for illicit narcotics
trafﬁc, but have never been named under the United States policy. This might imply that
these countries, while perhaps ﬁtting the deﬁnition of a major in terms of the quantity of
drug trafﬁc through their country, are making every effort to prevent this problem.
However, the issue of how the United States determines which countries will and will not
be named under this policy is a point of contention for some critics of the policy because
it is the point where ‘politics as usual’ enter into the policy. Consider this quote from a
recent story published by the United Press International. “The problem, according to its
[the drug policy] critics, is that the process allows the president to waive all of these
penalties for America's friends even if they are willfully neglecting their responsibilities
to apprehend drug smugglers” (UPI 2001). As will be argued in the theoretical
explanation and shown in the empirical analysis, there appear to be factors other than the
central issue of narcotics trafﬁc that are affecting the United States decision to threaten,
impose and ﬁnd ultimate success or failure in the use of these sanctions.

In the next section a generalized formal model of threats of sanctions is presented.
As the general model is presented, it should be clear how each step in the United States

drug sanctions policy ﬁts into the general threat model.

111

Developing A Formal Theoretical Model of Threats of Sanctions

In the chapters that follow, a formal theoretical model is developed and
transformed into a statistical model for estimation following the work of Signorino
(1999A, l999B, 2001) and Leblang (2001) on strategic probit models. Since the actions
of senders and targets of sanctions are considered to be strategic in nature (i.e., the
sender’s expected utility for different outcomes is inﬂuenced by the actions of the target
and vice versa) the model is estimated in a strategic probit framework provided in
STRAT, a program for analyzing statistical strategic models, provided by Signorino
(2001b).

In this section, a formal strategic model is presented and solved that shows the
conditions under which sender states will be expected to threaten and/or use economic
sanctions. This model was developed with the idea of testing it using a data set of all
cases of United States sanctions against countries that are found not to be cooperating in
the war against illegal drug trafﬁcking. Consistent with past theoretical work on
economic sanctions (Smith 1997, and Morgan and Miers, 1999), the main elements of the
game are the value of the policy under dispute, the cost of sanctions to the sender and the
target, and the value of relationship between the sender and target.

Games of complete information are presented for a “resolute” and “irresolute”
target along with an incomplete information game where the sender does not know the
target’s level of resolve. Figure 4-5 shows the incomplete information version of the

game.

112

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113

Looking at the top and bottom branches of the game separately the complete
information version can be seen. The top branch of the game in ﬁgure 4.5 has the
cardinal payoffs for an irresolute target, which has a lesser value for a sanctions stalemate
than the resolute target seen in the bottom branch of the game. Based on differences in
audience costs for democratic and autocratic regimes discussed in the previous section,
resolute targets are those with democratic regimes and irresolute targets are those without
democratic regimes.

Substantively, the most important elements of the game are the cost of sanctions,
the value of the policy at dispute, and the type of political institutions in the target
country. Smith (1997) shows that domestic factors as well as economic factors are
important in determining the overall value of using sanctions. Smith discusses the
implications of domestic costs associated with sanctions for senders, but does not take up
the issue of domestic costs in the target of sanctions. In this analysis, domestic factors in
both the sender and target are considered by incorporating domestic factors as well as
economic factors into the cost of sanctions.

In the incomplete information version of the game the sender does not know the
target’s resolve for standing up to the sender’s sanctions. The target’s resolve is
determined by how much the target values the policy which the sender is trying to change
relative to the size of the cost that the sender is able to impose on the target for not
complying. The cost of sanctions is determined not only by the value of the economic
commerce that is interrupted, but also the effect of those losses on political leaders’

ability to retain ofﬁce.

114

The essential elements represented in this analysis are: r, 0', R,CT,CS.

T represents the value of the policy at dispute for the target. This represents the
importance the target places on the issue over which sanctions are threatened or imposed.
For example, if sanctions are threatened or imposed over nuclear weapons proliferation,
then this continued policy has some value for the target. 0' represents the value to the
sender of altering the policy under dispute. For example if the sender threatens sanctions
and gains compliance from the target, the sender achieves value 0' without paying the
costs of imposing sanctions. R represents the value of the relationship between the sender
and the target. Countries with similar ideological preferences in international relations
have a higher amount of value for the existing relationship and face greater reputation
costs in terms of R from the failed threat of sanctions. Thus, there are costs involved in
sanctions at the threat stage, not related to the economic costs of the imposition of
sanctions. C r and Cs represent the cost of sanctions to the sender and the target of the
imposition of sanctions. Sanctions are not a costless policy option to the target or the
sender of sanctions. The very nature of sanctions, which restrict normal trade patterns
between targets and senders, is to force ﬁrms to choose sub optimal patterns of trade.
Van Bergeijk makes this case in his discussion of the costs of sanctions. He states, “The
most obvious and visible direct costs entail additional ﬁnancial and real outlays that are
immediately related to the imposition of sanctions. Examples are rising transport costs
due to the fact that trade may only be possible with more distant markets (for long sub-
optimal routes) and risk premiums that are to be paid to middle men and sanctions
busters” (van Bergeijk, 1985: 450). While these costs are deliberately imposed on the

target country, they are also, by necessity, imposed on ﬁrms operating within the sender

115

nation as well. The other relevant factor in the game, which is normalized to 0, is the
value of the status quo with no sanctions imposed. This is the value that the sender gets if
it chooses not to impose sanctions and accept the current situation, or the value that the
target gets if it complies immediately with the sender’s threat to sanction.

These essential elements to the game can be grouped into three categories of
importance to sanctions mentioned above: the cost of sanctions, the value of the policy
and the value of the relationship. The cost of sanctions is speciﬁc to the target and sender
nations and represented in the game by CT and C5. The value of the policy is also speciﬁc
to the sender and the target and is represented by 0' for the sender’s value and T for the
target’s value. The value of the relationship between the sender and target is dyadic in
nature and is meant to capture the country’s similarity of preferences in international
relations. The value of the policy to the sender and target and the costs that sanctions can
impose are meant to capture the potential asymmetric nature of the relationship, while R
is meant to capture similarity in preferences.

There are ﬁve potential outcomes that can be reached in the game with the
following payoffs to the target:

no, r—R, 0—C,, r—2C,

These values represent the following outcomes:
2' = The sender makes no threat and the target continues its policy.
0 = the target or sender concedes the issue with no sanctions imposed. Normalized

to zero.

116

r — R = The target continues its policy but only aﬁer the sender threatens sanctions and
then backs down. Thus the target gets the value of the policy minus any
reputation costs for being indicted as a violator of international norms.

O— C, = The sender imposes sanctions and after suffering one period of sanctions the

target gives in and changes its policy. Thus the target gets the normalized
value for not achieving its goal minus the costs of enduring one period of
sanctions.

2' — 2C7. = The sender imposes sanctions and the target stands ﬁrm. Thus, the target gets

the value of maintaining its policy minus the cost of sanctions. In annual data
on sanctions (like is used in the following empirical analysis), even though
sanctions have one start date and one end date, the sanctions policy could end
or continue during any year. Therefore, you can think of sanctions in two-year
moving blocks. The two-year block is arrived at in the following way. When
the target chooses not to comply with the demands of the sender, sanctions
will automatically be imposed for the following year. Thus even in the
successful sanctions case, once sanctions are imposed they will last for one
year. If the target changes its policy some time in the next year, sanctions will
be lifted at the end of that year. Therefore, a country that is not swayed to
change its policy during the ﬁrst year of sanctions will not have the sanctions
lifted in the following year either — Until the decision date comes up again.
This makes the multiplier two for sanctions that are only considered (or
observed) annually. If the target does not change its mind and sanctions are

imposed again the following year that new sanction will also be imposed for at

117

least two years if the target does not change its mind during the ﬁrst year of

the new sanctions.

Restrictions Over Preferences
The ﬁrst restriction is that a rational target must prefer getting to maintain its
policy with no added cost better than any other outcome:

r>(o, r-R, O—CT, T—ZCT).

Next, complying with a threat of sanctions must be preferred to blufﬁng and then
being forced to comply after suffering some period of sanctions. This should be true even
if the sanctions are of a very short duration:
0> O—CT and r >O> O-CT.

It must also be true for any rational target that maintaining its current policy after
being threatened by sanctions is preferred to maintaining that policy and actually being
sanctioned:

r—R > T—ZCT and r > r-R > T-ZCT.

One last restriction is that the target must value maintaining its policy aﬁer being
threatened by sanctions more than it does losing its policy after a short period of
sanctions. This has to be true because in both instances the target suffers the ill effects of
being singled out as violating some international norm, but in the latter case the target
also suffers any economic loss of the sanctions themselves. This is compounded by the
fact that in the latter case the target has to eventually change its policy, while in the
former, the target is allowed to maintain its policy:

r—R>O—CT.

118

Given these logical restrictions over preference orderings for the target, it is clear
that the only room for variation is in whether the value of the policy minus costs
associated with being a target ( 2' — 2CT) or potential target (I - R) of sanctions is greater
than or less than the value of losing the policy (0), or losing the policy after a struggle

(O — CT ). When the value of the policy is low and the costs imposed by sanctions are

high, targets are more likely to prefer losing the policy without a ﬁght to maintaining the

 

policy and facing the costs imposed by sanctions. Thus, targets who value the attainment
of their policy more than conceding the issue, regardless of the cost that the sender can
inﬂict through sanctions, are characterized as resolute targets. Irresolute targets, on the
other hand, would rather concede the issue, even after brief sanctions than face the costs
of continuing sanctions. Even the reputation costs alone are greater than the value of
maintaining the policy for irresolute targets. Thus, compliance at the threat of sanctions

is preferred to any outcome implicating the target as a violator of the policy in question.

 

 

 

 

 

Target Preference Orderings Over Outcomes (5 is most preferred and 1 is least preferred)
Target Type 5 4 3 2 1
Target Irresolute __
T O z' — R 0 - CT 2' 2 CT
(1 -P)
Target Resolute _ 2' _ 2 C _
LPL T 1' R 1' O 0 Cr

 

 

 

 

 

 

 

Since the sender is always the United States in this game (Remember, the game is
designed for testing on US Drug Sanction data), I will assume that the target has perfect
information regarding the sender’s payoffs for the possible outcomes (following the
convention of Smith 1997). Further, I will assume that there is only one type of sender.

The United States’ preference orderings are characterized as follows. The best outcome

for the sender will always occur when the target gives in at the mere threat of sanctions

119

0'. The second most preferred outcome for the sender comes from using sanctions and

forcing compliance from the target a“ — CS. In this outcome the sender achieves the

value of the policy, but at the cost of imposing sanctions for one period. The next most
preferred outcome for the sender is the status quo outcome, which is normalized to O.

The sender must prefer this to threatening sanctions and then being forced to back down
and receiving the payoff O — R. The least preferred outcome for the sender is a prolonged
stalemate where the target does not change its policy and sanctions remain in place
indeﬁnitely O — CS. Thus, the sender’s preference orderings are as follows:

a > cr— CS >O> O—R > O— 2C3.

These preference orderings reﬂect the assumption that the value of the policy is
large relative to the cost of sanctions for the sender. Thus, the value of the policy minus
the costs of imposing sanctions is assumed to be greater than the value of just looking the
other way and doing nothing. This assumption is particularly relevant to the sanctions
policy undertaken in the following chapter because the sender is always the United States
— a powerful trading state that likes to play an active role in world politics. While this is a
somewhat limiting case (just looking at the United States as the sender), the typical
sender of sanctions tends to ﬁt this classiﬁcation (economically powerful, internationally
active.) In other cases, where the sanctions are more severe and the economies of the
sender and target are more evenly matched, this restriction on preference orderings may
need to be loosened. This would allow for instances where achieving the desired policy

change from the target is not worth the cost incurred from sanctions.

120

Solving the game with complete information

Solving for the equilibrium when the target is resolute or irresolute and the sender
has complete information over the target’s type shows that under these conditions
sanctions will never occur. When the target is resolute, and the sender knows it, the
sender will never make a threat. This holds because, for the resolute target, the strategy
(Resist, Resist) weakly dominates all other strategies. The best reply to this strategy by
the sender is any strategy that involves not making a threat, either (no threat, sanction), or
no threat, no sanction). This outcome can also be reached by using backward induction.
At the last decision node, if a resolute target were sanctioned it would prefer to resist the
sender’s demands and hold out. Moving backward, the sender would prefer to back down
from its demand rather than sanction with no hope of gaining compliance. Knowing that
the sender would back down, the target will resist the sender’s initial threat of sanctions.
Finally, realizing that if it threatens sanctions, it will be forced to back down later, the
sender will choose not to even make the threat of sanctions.

When facing an irresolute target, sanctions also never occur in equilibrium, but in
this game the sender gets its way at the mere threat of sanctions. The full set of equilibria
for the game are: (T,S; C,R), (T,S; C,C), (T,~S; C,R), (T,~S; C,C). In this game,
cooperate on the ﬁrst move is a weakly dominant strategy for the target whether it
cooperates on the second move or not. Thus, given that the irresolute target will always
cooperate on it s ﬁrst move, the sender will always threaten on its ﬁrst move. Since the
target will always cooperate at its ﬁrst move and the sender will always sanction (threat,
cooperate) is the only outcome on the equilibrium path. This can also be easily shown

through backward induction as was demonstrated in the case of the resolute target.

121

On the face of it, the complete and perfect information results do not appear to be
particularly useful. We know, for example, that sanctions do occur in the real world, but
in these games sanctions never occur. As will be demonstrated, though, in a situation that
more closely resembles the real world with incomplete information, any outcome is
possible with some probability. The lesson to be taken from these games of complete and
perfect information is that the vast majority of disputes in which sanctions might be used
probably are resolved at the threat level. Based on these results, there is also no reason to
assume a priori that this should bias the success rate of the sample of sanctions cases
observed in any particular direction. Based on the equilibria of this game, some cases of
sanctions are not observed because the target backs down at the mere threat of sanctions,
while others are not observed because the sender backs down after threatening sanctions.
The likelihood of an observed sanctions case being a success should be biased upward
because when the sender does not think it will be successful it backs down at the threat
stage. On the other hand the success rate of observed sanctions should be biased
downward because targets that think they will be forced to comply with sanctions once
they are imposed will back down aﬁer being threatened. The actual direction of bias, if
any, is an empirical question that will be investigated in this study.

Solving the Game With Incomplete Information

Figure 4-5 gives the extensive form of the sanctions game. The two actors are the
sender (Sen) and the Target (Tar). The sender has the ﬁrst move in the game and must
decide to threaten sanctions or not to threaten sanctions. If the sender does not threaten,
the status quo persists. If the sender does threaten sanctions, the target must decide to

resist the threat or comply with the demands of the sender. Resisting the threat leaves the

122

sender with the decision to back down from its threat or to carry out the threat by
imposing sanctions. Backing down prevents sanctions from being imposed, but at the
cost of losing the value of the disputed policy plus additional “loss of reputation” costs.
The preference orderings for each side are the same as described in the complete
information version of the game. The incomplete information in this game arises because
rather than make an assumption about the target’s resolve for sanctions (i.e., its utility for
sanctions), the model makes it uncertain. The ﬁrst move in this model is assigned to
nature and determines T’s type. This, in turn, determines the preference orderings for the
target. The lower branch of the tree is the case of a resolute target, which occurs with
probability P. As explained earlier, the resolute target has the following preference

orderings: T > r—R>Z'—2CT>O>O—CT. The upper branch is the case ofthe

irresolute target. This occurs with probability (l-P) and produces the following

preference orderings for the target: I > r - R > O > 0 — C7. > 2' — 2CT. The target knows

the outcome of the chance move (and thus, its type), but the sender does not. The
information sets in the game represent the sender’s lack of knowledge regarding the
target’s resolve, which it updates as the sanctions game progresses. Initially, the sender’s
belief that the target is resolute is the same as the probability of the lower branch in the
initial move by nature; P denotes this probability. This is true because “Beliefs must be
consistent with the structure of the game, including chance moves” (Morrow, 1994;
p.201). It may seem odd to think of nature determining whether the target of the sender’s
potential sanctions is resolute or not, when in reality the target is either resolute or it is

not. A better way to think of this is that the chance probabilities represent the uncertainty

123

in the sender’s mind. The sender has to consider both the possibility that it is facing a
resolute and an irresolute target.

In the complete and perfect information versions of this game we saw that when
the target is irresolute, it will always comply with the sender’s threat and the sender will
always make the threat. The resolute target, on the other hand, has a dominant strategy to
resist any threats of sanctions. This leads the sender not to make a threat in equilibrium.
. When the sender has incomplete information regarding the target’s type, an irresolute
target may have an incentive to resist the sender’s initial demand in the hope that the
sender will back down in the mistaken belief that the target is resolute. This, in turn,
leads the sender to occasionally consider sanctioning a target that resists the sender’s
threat in order to test whether the target is truly resolute or just an irresolute target that is
bluﬁing. Since the sender can be expected to misjudge the target’s true type with some
probability, we can expect to see sanctions occur with some probability. Another way to
think of this is that, as a potential target of sanctions, even if you are irresolute and know
it you may want to test the sender from time to time by blufﬁng.

The game is solved using the Bayesian equilibrium technique. This constitutes a
strategy and a set of beliefs for each player at every node. Equilibrium is attained if each
player’s strategy is a best reply to the other player’s strategies in each sub game.

A resolute target will always employ the pure strategy (resist, resist) since it
weakly dominates all other strategies regardless of the strategy played by the sender.
However, the sender and the irresolute target will adopt mixed strategies in this game.
The sender will adopt a mixed strategy designed to make an irresolute target indifferent

between resisting and complying at its ﬁrst decision node. The irresolute target will also

124

mix its strategy to make the sender indifferent between threatening and not threatening
sanctions. Therefore, if the game reaches the Sender’s second decision node, it is
uncertain whether the potential target of sanctions is a resolute target that will not give in
to sanctions or if it is facing an irresolute target that is blufﬁng and will comply with the
sender’s demands once sanctions are imposed. The sender will impose sanctions if its
expected utility for sanctioning exceeds its expected utility for backing down. In order to
determine its expected utility for sanctions, the sender must take account of its updated

belief that the target is resolute. The sender’s updated beliefs are indicated by

R and (1— R) . The bar above the P indicates that it is an updated belief. The critical

belief, 17m, , occurs when the sender is indifferent between backing down and sanctioning.
This critical belief is found by equating the sender’s expected utility from backing down
to its expected utility for sanctioning. Regardless of which type of target it is facing, the
sender achieves O-R for backing down. The sender will only choose to sanction

if 7’ < 13;", .

17....(0-2CS)+(1—r7.,,-,)(o--CS)=0—R
—2CI_’ +o—Cs—I7 az-R‘

3 cri! crit

 

_Cs-P:rit — Pcrita = -R + C3 — 0-
Eri,(-C —0')= C —R-0'
— _ C, — R — 0'
cril _Cs _ 0'
If R > Rm, , the sender prefers backing down to sanctioning. This result matches

expectations that the higher the sender’s belief that the target is resolute, the less likely it

will want to use economic sanctions.

125

Proceeding through the game backward brings us to the target’s decision to resist
or comply with the sender’s threat to sanction. All targets will prefer the sender backing
down from its initial threat to complying with the threat. Resolute targets will also prefer
a sanctions deadlock to complying with the sender’s threat so they will always resist at
this stage. As noted earlier, the target has complete information so the target knows its
own resolve for sanctions. Knowing this, a resolute target will always employ the pure
strategy (resist, resist) since it weakly dominates all other strategies regardless of the
strategy played by the sender. The irresolute target does not have a dominant strategy so
it must judge the response of the sender. Ifthe sender will back down the irresolute target
wishes to resist. If the sender will carry out its threat and impose sanctions, it wishes to
comply with the threat and end the dispute with no further costs of sanctions imposed.
Now we must ﬁnd the probability, r, of the sender’s carrying out its threat that will make
an irresolute target indifferent between resisting the threat and complying with the threat.
The irresolute target gets the normalized value of O for complying with the sender’s
threat. If it resists and the sender does not sanction, the target gets I — R . If the sender
does sanction the target, knowing that it is bluf’f'mg and will comply with sanctions once

instituted, will get 0 - C,. Thus, I solve for r as follows:

O=r(z'—R)+(1-r)(O—C,)
O=r(r-R)—C, +rC,
r(r—R)+rC, = C,

r(r--R+C,)=C,
C,
r:—
r-R+Q

If the probability that the sender will carry out its threat of sanctions is greater

than the value of r above, then the irresolute target will comply with the sender’s threat.

126

If it is less, then the irresolute target will resist. If the sender adopts a mixed strategy that
imposes sanctions at exactly the indifference probability above, and backs down with a
probability of l-minus the indifference probability, irresolute targets will be made
indifferent between resisting and complying. The sender must have beliefs that equal

Rm, -- the belief calculated above that makes it indifferent between sanctioning and

backing down — to play this mixed strategy. Therefore, I must ﬁnd the probability, q, of

an irresolute target resisting sanctions that will make the sender indifferent.

_ p(T resolute) p(r IT resolute)

cn’t -

 

p(T resolute) p(r IT resolute) + p(T irresolute) p(r IT irresolute)

 

13.: P“)
P<1)+(1-P)q
_P(1- __._'P..>
(1— HP...

As a ﬁnal step, I can substitute into the last line of the calculation above in terms of the

outcome the sender achieves given Pm, and (1- PW).

_P(1- P...)_ P(0-2C.)
(1-P)P (1-P)(0'-C.)

 

 

CHI
So far, best reply correspondences have been found for all but the sender’s ﬁrst
move in the game. The next question is when will the sender threaten sanctions and

when will it be satisﬁed to live with the status quo? If the sender’s initial belief P is

greater than P then the sender expects that the target will resist any threat of sanctions.

CH! ’

127

According to earlier assumptions, the sender prefers the status quo outcome to a sanctions
deadlock, but prefers sanctions in which it gets its way after a short period to the status
quo. Therefore, when P > PC", the sender will prefer to accept the status quo rather than
threaten sanctions believing that it would either be forced to back down in the future or
live with a sanctions deadlock. On the other hand, if P <Pm, the sender will not
necessarily be deterred from threatening sanctions and testing the resolve of the target.
The sender will compare the value of threatening, which is calculated from the target’s
mixed strategy, with the value of the status quo. This results in the sender being
indifferent between sanctioning and backing down when the target resists the initial
threat. Since the sender is made indifferent between sanctioning and backing down in its
second move I can use its utility for backing down, which is simply O-R. The sender will
prefer accepting the status quo to making a threat whenever its utility for the status quo

exceeds its utility for threatening. This is calculated as follows:

O> P(O-R)+(1-P)(q(O-R)+(1-q)0')

PR>(1-P)[( P(O'ZCS) )(O—R)+(1— P(O'ZCS) )0]
(l-P)(0-C.) (l—P)(a-C.)

“00%“ 2CSRP ]+[ a—zapc, D
(1-P)(0-C.) (1-P)(0-Cs)

 

 

 

 

128

 

PR>(1_P)[2CSRP+a—20PC5]

(1-P)(0-C.)

 

PR>(2CSRP+a—20PC5]

(a-Cs)

2CsR—20Cs 0'
R > +
(CT-C.) P(U-C.)

 

Solving for P gives the following conditions for the sender’s initial beliefs:

 

0' 2CsR—20Cs
—— < R-
.P(0'—CS) (a—Cs)

%<R-2CsR—20Cs

a < (R-ZCSR—20C5)P

P > 0’
R-2CsR—20CS

 

The above analysis leads to the following equilibria:
When

P> “ ,
R-ZCSR—ZoCs

 

(~ T,~ S;r,r:p) with

— CS-R-O'
p>Pcril =
—Cs—0'

is a perfect Bayesian equilibrium.

129

When

< 0'
R—ZCSR —2oC,

T, ———C'—-—S, 1-——C—’——-~S ;r, LC, Mr 1PM,
r—R+C, r-R+C, (1-P)P.m (l-P)Pm-.

with

 

— _ Cs-R-O'

-C,—0'

is another perfect Bayesian equilibrium.

Implications of the formal model

Now that the formal model has been developed, predictions can be formally
derived about several issues that are of interest to this study. The primary focus of this
section of the dissertation is on what occurs prior to the actual implementation of
sanctions. As was discussed earlier, there are three ways that a potential sanctions case
could end prior to the observed implementation of sanctions. First the sender may be
deterred from threatening sanctions, allowing the target to continue its policy unabated by
sanctions. Hypotheses regarding deterrence of the sender are covered in Hla-c. Second,
the target may back down at the threat of sanctions allowing the sender to achieve its
policy goal without implementing the threatened sanctions. Hypotheses regarding
successful threats by the sender are covered in H2a-c. Third, the sender may threaten
sanctions only to back down later against a deﬁant target. In this case the target gets to

continue its policy without being sanctioned although it suffers reputation costs

130

associated with been threatened. Hypotheses regarding failed threats by the sender are
covered in H3a-c.

These three ways of ending sanctions at the threat stage lead to three questions for
the model. First, when do we expect sender nations not to threaten sanctions (the target
deters the sender from making a threat)? Second, when do we expect targets to comply at
the threat stage (a successful threat of sanctions)? Third, when do we expect senders to
back down after they have made a threat (an unsuccessful threat of sanctions)? Within
the theoretical framework for this study, I also generate hypotheses about how domestic
political institutions affect these decisions. Once these fundamental questions have been
answered, we can proceed to investigate how the threat stage of sanctions would be
expected to affect the likelihood of success in cases of sanctions that are observed. If the
target is going to comply, is it more likely to comply to a threat of sanctions or with the
actual implementation of sanctions? In other words, given that a threat has been made,
what factors affect the sender’s decision to impose actual sanctions?

Up to this point the speciﬁcation of the formal model and the discussion of the
implications of that model have been purely theoretical and at a very general level.
However, as explained at the beginning of this chapter, the end goal of this model is that
the theoretical implications be tested empirically with historical data from the United
States drug sanctions policy. Therefore, each of the hypotheses generated in this section
will be presented in a theoretical form, as T-Hl , 2, 3..., with a corresponding operational
hypothesis noted as O-Hl , 2, 3... The operational hypotheses are related to the United

States Drug Sanctions policy described earlier.

131

 

 

f:

1h

sir

The equilibria calculated in the above analysis can be used to compute the
probabilities of each of the events in the three questions above. We can start by assuming
that P (the probability that the target is resolute) is distributed uniformly on [0,1]. Now
from the equilibria I know that the sender will always threaten sanctions when

P< ‘7 ,
R-2CsR-20Cs

 

and will be deterred from threatening sanctions whenever

> 0'
R-ZCSR-ZoC, '

 

Therefore, the probability of the sender threatening sanctions is the probability of the

former condition holding. This can be represented as:

Pr(threat) = Pr(P < P.) — ‘T— = — = P ,
I 1 dx 1
0
where
. 0'

 

= R -2CSR — 20c,

The numerator in the above integral equation is the probability that P < P‘. That
is, when P< P', the sender’s belief that the target is resolute is small enough that it would
threaten sanctions with the belief that the target will back down. The denominator is
simply the probability of all possible values of the target’s resolve, P. Both the numerator
and the denominator in this equation are calculated using deﬁnite integrals30 with P

assumed to be distributed unifonnly on [0,1]. The probability of the sender threatening

132

sanctions, then, is just the fraction of P’s that are below P‘. Thus, I have an answer to the
ﬁrst question. I expect to see senders threaten sanctions when their initial beliefs
regarding the resolve of the target are such that P< P‘, and senders will be deterred from
making a threat when P> P’.

At this point we can look further into the conditions necessary for the sender to
threaten sanctions. The ﬁrst thing we notice is the rather unexpected ﬁnding that the
sender’s decision to threaten sanctions is determined primarily by its own characteristics,
not factors speciﬁc to the target. One potential reason for seeing this is that the game to
this point has no history and the sender has not had an opportunity to observe any signals
from the target about how it will respond to a threat of sanctions. In ﬁgure 4.6,
theoretical values of the policy at dispute are varied from minimum to maximum values
(scaled from 0 to 1) to see how the sender’s value for the policy at dispute affects its
decision to threaten sanctions. In this ﬁgure we see that the greater the sender’s value for

the policy the more likely it is to threaten sanctions.

#-

3° The deﬁnite integral gives the exact area under a continuous curve y=f(x) between values of x on

the interval [a,b].

133

Figure 4.6: Effect of Changing Value of Policy on Decision to Threaten Sanctions

T-HI a:

 

Values of P*

   

I High Cost
I Mid Cost

ULow Cost_

 
 
       

  

Low Cost
High Cost

0.1 0.2 0.3 0.4 0.5 0.7 0.8 0.9
Increasing Value of Policy (Slgma)

 

 

 

This graph depicts how the value of the policy is expected to affect whether the sender
decides to sanction (while holding the expected economic and political cost of the
sanctions constant at high, medium, or low). Recall that as the value of P' gets larger it
becomes more difﬁcult for the target to deter the sender from threatening sanctions. As
the value of the policy gets larger the value of P. tends to get larger too. This is even
more pronounced when the cost of the sanctions in political and economic terms are not
expected to be very high. This seems logical since the value of the policy at dispute
would have a heavier weight relative to the costs of sanctions in these cases.

The greater the sender '5 value for the policy at dispute (0'), the more likely it is

to threaten sanctions.

Operationally, this leads to the expectation that the United States will be more

likely to threaten sanctions against counnies that have a larger amount of illegal drug

trafﬁcking. On a more general level, when issues are of great importance to the sender

nation, they are more likely to take action.

134

 

‘I

al'

10‘

ITa.
(Be

ﬁt:

O-Hla: The United States will be more likely to threaten sanctions against countries that
have a large amount of drug traﬂic through their country.

Next, ﬁgure 4.7 shows the expected effect of varying the costs of sanctions to the
sender while holding the value of the policy constant at low, mid, or high costs. Based on
the assumption that sanctions will have a negative cost to the sender both in economic
and political terms,3 I the costs are again scaled to represent a range from a minimum
value (0) to a maximum value (—1). The helps graph provides incite into how
expectations regarding costs will affect the decision to threaten sanctions. When the
value of the policy at dispute is low, as the expected costs of sanctions to the sender
increase from 0 to -1, the value of P. is strictly decreasing. Recall that as the value of P"'
gets smaller the sender is less likely to threaten sanctions. Therefore, as the expected
costs increase the sender will be less likely to threaten sanctions when the value of the
policy is of little importance. When the value of the policy is mid level, the expected
costs of sanctions tend to have no deterrent effect on the sender. When the value of the
policy is at its highest, varying the costs of sanctions produce a P. that is decreasing but is
always higher than the corresponding values when the value of the policy is at its

lowest”.

 

3' As in the previous section this assumes that all sanctions are a restriction in the normal ﬂow of
trade, and hence, result in inefﬁciencies that will bring about negative costs to both the target and sender
(Bergeijk, 1995).

32 The function for high values is discontinuous at exactly .5, so the graph shows a regression estimate

ﬁt to the true values.

135

 

0f

the

COL:

 

Values of P*

 

 

   
   
  
 

V

 

 

 

 

 

‘-0.5« uHigh Value
1.10. I .Mid Value
4.5, ' ‘ DLow Value
I...
l.2.5

.1 .
1-3.0
-3.5 I

0 ' ‘ High Value

Economic and Political Costs to Sender (Cs and R)

 

Z4. . . . .
I 0.0 -o.1 o2 -o.3 -o.4 as -0.6 -o.7 —0.8 41.9 -1.0
I

Figure 4.7: Effect of Changing The Expected Cost of Sanctions on the Decision to

Threaten Sanctions

Graph of how the expected economic and political cost of the sanctions are expected to
affect whether the sender decides to sanction (while holding the value of the policy (sigma)
constant at high, medium, or low). As the value of P' gets larger it becomes more difﬁcult
for the target to deter the sender from threatening sanctions. As the expected economic
and political cost of sanctions get larger the value of P. is generally decreasing.

T-Hlb: Assuming that sanctions will have a negative economic and political cost to
the sender, as the expected economic cost of sanctions to the sender increases
(C,), the likelihood of the sender making a threat decreases.

Sanctions tend to damage the relationship between the sender and target. The value
of the relationship between potential senders and target can be thought of in both political
and economic terms. This point is made by Smith (1997) and stressed throughout the
theoretical portion of this dissertation. The total amount of trade between the two

countries is certainly an important aspect of the economic relationship. For a sender

136

 

0n

Sane

nation that values the trading relationship it has with the potential target of sanctions,
sanctions as a form of punishment to coerce the target to alter its policy will not only do
economic harm to the target, but also to the sender nation. Consider that sanctions
restrict not only the trade of the target state but also the sender state’s trade with the
target. Thus, assuming that countries value their trading relationships, and would rather
not impose sanctions that could do potential long-term harm to the trading relationship
between the sender and target,33 they should be less likely to threaten and impose
sanctions against countries with which they engage in high levels of trade. Therefore, the
testable hypothesis corresponding to T-Hlb is:
O-HI b: The United States will be less likely to threaten sanctions against countries that it

has a lot of trade with.

The ﬁnal element for consideration is how much value the sender and target place

on maintaining the existing relationship. Figure 4.8 shows that the greater the value of
the relationship (R), the less likely the sender is to jeopardize that relationship by

threatening sanctions.

 

’3 See Lektzian and Souva (2000) for an analysis of the time it takes for trade to return to pre-

sanctions levels once sanctions have ended.

137

th

 

Values of P"

 

P. N. 0". V. *0. 0. N. 00. 0? O.
O O O O O O O O o ‘-
Political Value of Relationship (R)

 

 

l
l

 

j
I High Value & Econ Cost I Mid Value & Econ Cost CI Low Value & Econ CostI

 

1

Figure 4.8: Effect of Changing The Value of the Relationship on the Decision to
I Threaten Sanctions

The way (R) is entered into the formal model, when the sender has a high value
for the relationship the target does also. This is not the case for the cost term (Cs). The
reason behind the difference is that in economic terms the sender typically has a large
trading economy of which trade with the target makes up only a small element. On the
other hand targets generally conduct a much larger portion of their trade with the sender
of sanctions. Of course this does not have to be the case, but it serves to illustrate why
economic cost to sender and target need to be separate terms.

Regarding the political value of the relationship I propose that when the two
countries have similar ideological preferences they both place a high political value on

the relationship. This is true regardless of the size of the economies of the two countries.

138

Even the most powerful of states need friends and sanctions, which may potentially harm

your allies, will come at a high political cost.

T-ch: The more the sender values the political relationship with the potential target, the
less likely it is to threaten sanctions.

From a political perspective, sanctions are also expected to do harm to the
relationship between the target and sender. States that value the relationship they have
with another state should be less willing to use economic sanctions against it as a means
to alter its policy. In general, states with the same political regime type can be thought of
as having similar political ideals and value their relationship to a higher degree.

O-HIC: The United States will be less likely to threaten sanctions against states with
similar regime types.

The second question addressed with this model is when would the target of
sanctions be expected to back down at the mere threat of sanctions. Previously, Morgan
and Meirs also addressed this questioned. They concluded that resolute targets that know
they are resolute will never give in at the threat stage of sanctions. However, irresolute
targets will sometimes bluff at this stage and sometimes they will comply. When the
probability that the sender will carry out its threat of sanctions, r, is greater than the right
side of the equation below

C

l

r:————
r—R+Q

the irresolute target will comply with the sender’s threat of sanctions. Thus, as the value
of the policy to the target, 7 , increases, the right side of the equation decreases, making

the target less likely to comply with the sender’s threat. This leads to the following

139

hypothesis: As the target’s value for the policy becomes larger, it is less likely to give in
when threatened. A second interesting implication from this equation is that the cost that
the sender expects to be able to impose on the target Ct is not expected to affect the
probability that the target will comply with threat of sanctions. Finally the greater the
value of the relationship between the two countries, the less likely is the target to comply
with the sender’s threats.

Of course, the probability that the target will back down will vary with the level of
its resolve, P. Also, the target is not expected to consider backing down at all if it is

resolute. (l-P) is the probability that the target is irresolute, and from the equilibria

cm —P

W] is the criterion necessary for the irresolute
- cril

conditions calculated above,l:

target to back down. Therefore, I can calculate the probability that the irresolute target
will back down from the threat of sanctions as

(1_P)[ P....—P I; -P.

(1 - Plim

Now, the probability that the target will back down given that the sender has

threatened sanctions can be calculated as

 

 

0
Pr(Complyl Threat) = . ”"

As I calculated earlier, the denominator of the above equation is the probability that P <
P. in the distribution and is equal to the value of P’.

The numerator is slightly more complex, and can be solved as:

140

A = J0};(x)dx

 

 

P.—x
x = CL"
f() Pm.
A=IP PCs—x61):
o P

Now taking the deﬁnite integral on the top leaves,

Po
.2

 

A=p‘—1_3
l

Placing this over the denominator of P. gives

 

 

. P
P — — e
A = For“! 1 _ E
P 2P

Substituting the values of the outcomes for the Sender shows us that the target will
comply with the sender’s threat of sanctions with probability equal to:

1+ 0(CS +0)
2(C, — R — 0')(—2CSR + R-20CS) '

 

Using Mathematica’s limit function to manipulate individual parameters in this
equation reveals that holding the cost of sanctions to the sender (Cs) and the importance
of the relationship (R) constant and decreasing the value of the policy at dispute to the
sender (0‘) to 0, causes the probability of the target complying with the sender’s threat to
increase toward 1.

T-H2a: As the value of the policy at dispute to the sender (0') decreases, the probability

of the target complying with the sender’s threat increases.

141

This hypothesis gives the theoretical expectation about the relationship between
the importance of the issue to the sender and the chance that the sender will be successful
at the threat stage without the imposition of sanctions. When the sender threatens
sanctions over an issue that is not that important, it is more likely to gain compliance at
the threat stage. In other words, as others have argued, it is more difﬁcult to gain
compliance with sanctions when the issue of great importance. Here we see that this is
also the hypothesized relationship when considering threats of sanctions.

O-HZa: Countries with a large amount of drug traﬁ‘ic will be less likely to comply with
the United States ’ threat of sanctions.

Also, as the economic cost of sanctions to the sender increase toward inﬁnity
(while holding the other two factors constant), the probability of the target’s compliance
goes toward 1.

T -H2b: As the cost of sanctions to the sender increase, the probability of the target 's
compliance given a threat increases.

According to this hypothesis, when the sender expects sanctions to be costly but
threatens anyway, the target is more likely to comply with the mere threat.
0-H2b: Countries that trade more with the United States will be more likely to comply
with the United States’ threat of sanctions.

When the ﬁnal parameter in the equation, R, representing the value of the
relationship, goes toward inﬁnity, the probability of the target complying with the
sender’s threat increases toward 1.

T-H2c: When the value of the relationship increases, the probability of the target

complying with the sender ’s threat increases.

142

According to this hypothesis, when the sender places a high value on the
relationship it has with the potential target, but threatens anyway, the target is more likely
to comply at the threat stage without the sender having to impose actual sanctions.
0-H2c: Countries with a similar regime type to that of the United States will be more
likely to comply with the United States ’ threat of sanctions.

The third theoretical question addressed is, when we would expect to see senders
back down or carry out their threat after making the initial threat of sanctions. The

probability that sanctions are actually observed when P < P. is Pr(Target Resists”)

 

Pr(Sender Sanctions) = (.1: I 2" C ). Since P only occurs in the numerator of this
I - + ,

crit
equation, I can see that the probability of sanctions being observed increases as the
sender’s belief that the target is resolute grows. This occurs because the irresolute target
is more likely to bluff as the credibility of its resolve grows.

The probability of sanctions occurring once a threat has been made can be

calculated as the probability of sanctions, shown directly above, given that a threat

 

 

 

 

occurred, P';

P. C,x

0 — . —
Pr(SanctiomI Threat) .__ Pm. (Z: R + C,)

I 1 dx
0
C: (P .)2

Pr(SanctionsI Threat) = 2pm: (Ci; R + r)

143

C,0'

 

 

Pr(SanctionsI Threat) = C R 'RZCSR - 20Cs
2[;:__:EJ(CI __ R+ T)
'C; _ 0.

Replacing P* and PM, with their values in terms of the outcomes gives

__ CMQ+®
2(R+0:—Cs)(-R+2(R+0‘)Cs)(R-t—C,)

 

This equation is also not easy to work with, but using Mathematica to manipulate
the formula as before reveals how the probability of sanctions given a threat is predicted
to change when certain parameters in the equation are taken to their theoretical limit.

First, as the economic cost to the sender goes toward inﬁnity, the probability of
sanctions given a threat goes toward 0. Also, as the cost to the target goes to 0, the
probability of sanctions given a threat goes toward 0.

T-H3a: As the cost of sanctions to the sender and target increases, the probability of
sanctions given a threat decreases.

This leads to the following operational hypothesis:

0-H3a: When there is more trade between the sender and target, the probability of
sanctions given a threat decreases.

Next, as the sender’s value for the policy at stake goes to O, the probability of

sanctions given a threat goes toward 0.

 

Fem "P — P

is. _E

ml

 

3’ Pr(Target Resists) = l—Pr(Target Complies) = 1- This includes both the

 

tit

probability of resolute and irresolute Targets resisting.

144

T ~H3 b: Generally, as the sender ’s value for the policy at dispute decreases, the
probability of sanctions given a threat decreases.
This leads to the following operational hypothesis:
0-H3b: When there is less drug traﬂic in the target, the probability of sanctions given a
threat decreases.

Finally, as the value of the relationship goes toward inﬁnity, the probability of
. sanctions given a threat goes toward 035 .
T-H3c: As the value of the relationship increases, the probability of sanctions given a
threat decreases.
Resulting in this ﬁnal operational hypothesis for testing:
0-H3c: When states have similar regime types, the probability of sanctions given a

threat decreases.

 

3’ All of these outcomes were generated using the Limit[ ] function in Mathematica. Mathematica

evaluates the equation as a particular variable moves toward the value speciﬁed.

145

Summary of Testable Hypotheses Developed in this Chapter
Theoretical Hypotheses Not Genergted from the Formal Model

H1: Sanctions that are threatened against democracies should be more likely to be
successful than those threatened against other regime types.

H2: Sanctions that are actually imposed against democracies should be less likely to be
successful than those imposed against other regime types.

H3: In general, senders should be more successful at using economic force to coerce
targets to cooperate at the threat stage than at the implementation stage.

Stage One of Sanctions Model — Making a Threat:
O-Hla: The United States will be more likely to threaten sanctions against countries that
have a large amount of drug trafﬁc through their country.

O-Hlb: The United States will be less likely to threaten sanctions against countries that it
has a lot of trade with.

O-ch: The United States will be less likely to threaten sanctions against states with
similar regime types.

Stage Two of SganctionS Model — Compliance with Threat:
O-H2a: Countries with a large amount of drug traffic will be less likely to comply with
the United States’ threat of sanctions.

O-H2b: Countries that trade more with the United States will be more likely to comply
with United States’ threat of sanctions.

O-H20: Countries with a similar regime type to that of the United States will be more
likely to comply with the United States’ threat of sanctions.

Stage Three of Sanctions Model — Implementjation of Threat:
O-H3a: When there is more trade between the sender and target, the probability of
sanctions given a threat decreases.

O-H3b: When there is less drug traffic in the target, the probability of sanctions given a
threat decreases.

O-H3c: When states have similar regime types, the probability of sanctions given a threat ’
decreases.

146

Chapter 5
Threats of Sanctions: Converting the Game Theoretic Model to an Empirical Model

In the previous section a theoretical argument was developed which describes the
factors that should be influential in the study of threats and uses of economic sanctions.
Several theoretical hypotheses were developed which predict how states will behave
based on how these factors are expected to affect state behavior within a simple theory
regarding the use of sanctions as anticipatory punishment.

One thing that should be evident from the logic of the previous section is the
importance of strategic interaction between targets and senders of sanctions. What is also
clear is that previous methods of modeling the sanctions process have not included
strategic behavior. Previous dyadic models of sanctions make the implicit assumption
that dyads are making decisions, which fails to account for sender - target strategic action.

Signorino (1999) notes that states do not act in a vacuum and shows through
Monte Carlo estimates that “if observed actions are the result of (perhaps complex)
strategic interaction, then it is unlikely that a simple logit functional form will capture the
structure of that strategic interdependence” (Signorino 1999; p.280). Because of this,
Signorino shows, “logit analysis of strategic interaction can lead to parameter estimates
with wrong substantive interpretations: Fitted values and predictions of outcome
probabilities can be grossly incorrect, as can calculations of the effects of variables on the
changes in outcome probabilities” (Signorino 1999; p.280). Signorino (1999)
demonstrates that misspeciﬁcation bias is also introduced in statistical analyses by

modeling strategic processes with inherently non-strategic methods of analysis.

147

 

 

tha
165'
the

SCC

IhCI

stra
beti
stari
acto
acto
choc

pie SI

On more theoretical than statistical grounds, Signorino also makes the assumption
that the ultimate goal of the social scientist in performing statistical analysis is theory
testing. To that end, if at all possible we would like to have a direct link between the
theoretical model and the statistical model we use to test it. For those reasons, in this
section, I attempt to test a statistical model that comes as close as possible to the
theoretical model that was presented in the previous chapter.

The game theoretic model developed in the previous chapter incorporates the
strategic nature of international relations by modeling the structure of the relations
between the target and sender of sanctions. That model will now be transformed into a
statistical model by specifying the error component around each decision made by the
actors in the model. As Signorino notes, a theoretical model should identify the actor or
actors, their sequence of choices, their options, the outcomes, and their incentives for
choosing particular actions or outcomes. (Signorino 2000; p.4) All of these elements are
present in the theoretical model and have an analogous component in the statistical model
that is developed. In essence, the statistical model used is the same as the theoretical
model only with the introduction of a random component that includes a probability
distribution over the outcomes, which guarantees probabilistic outcomes. This is crucial,
because if the probabilities associated with any outcomes in the model are zero,
maximum likelihood estimates are impossible to attain. The general likelihood function

that is maximized for hypothesis testing is:

L(ﬂly) mf(ypy2,---,y~|x,ﬂ)
=f(}’1lxr’mf0’2lx2’mmf0’rlemﬂ)

148

 

 

[me

like

esti

CCO

In equation 2 it is clear that the likelihood equation will be reduced to zero if the
likelihood of any event is zero. Therefore L(,BI y) = OVﬂ and maximum likelihood
estimation will be impossible“.

Figure 5.1 shows a general theoretical model of threats and implementations of

economic sanctions.

1- Sender

 
 
 
  
   
  
  
 
   
 
 

Threat

2- Target

Resist
Threat

1- Sender

Successful Sanction

Threat (Sender Sanction
backs
dow

2- Target

Failed Comply with Resist
Threat Sanctions Sanctions
Demand Demand
Successful gene:
Sanctions anc ons

Figure 5.1: Threat and implementation model of sanctions
In this model, as in the game theoretic model of the previous chapter, the actors are the
potential senders and targets of sanctions, their choices and outcomes are as described in

the different branches of the game tree in ﬁgure 1 and their decision rule for choosing

 

’6 Although a common trick is simply to raise each probability by a miniscule amount.

149

between outcomes or options is that they will make the choice that maximizes their
expected utility at each decision node.
Figure 5.2 shows a more general model of the structure of interaction between the

sender and target of sanctions, labeled 1 and 2 respectively.

1
a1 32
P1 P2
2
U1Y1 a3
34
P3 P4
1
U1Y3 a
U2Y3 5 35
P5 P6
2
U1Y5 a7 38
UzYs
P7 P8
u1y7 U1Ye
U2Y7 UzYa

Figure 5.2: General Structure of Sanctions Game

150

In this ﬁgure, the a’s represent actions, the U’s represent true utilities for each outcome
and the Y’s distinguish the outcomes at each terminal node. Assmning that each player
acts to maximize its utility at each decision node, I can deduce which outcomes will result
in equilibrium”. The following equations show the conditions under which each
outcome of the game will be achieved:

(Y1 ifUng > U2Y7 and UIYs > UlYg and U2Y3 > U2Y5 and UIYI > U1Y3, OI‘

ifU2Y7 > UzYs and U1Y5 > U1Y7 and U2Y3 > U2Y5 and U1Y1> U1Y3, 01'
ifUng > U2Y7 and UIYg > Ule and U2Y3 > UzYs and U1Y1 > U1Y3, 01'
ifU2Y7 > U2Y3 and U1Y7 > U1Y5 and U2Y3 > U2Y7 and U1Y1 > U1Y3, OI’
ifU2Y7 > Ung and U1Y5 > U1Y7 and U2Y5 > U2Y3 and Ule > UIYs, 01‘
ifUng > U2Y7 and UIYs > Ung and U2Y5 > U2Y3 and Ule > U1Y5, 01'
if Ung > U2Y7 and UIYg > U|Y5 and UzYs > U2Y3 and U1Y1 > UlYg, 01'
If U2Y7 > Ung and U1Y7 > UIYs and U2Y7 > U2Y3 and UIYI > U1Y7

 

Y3 IfUng > U2Y7 and UIY5 > UIYg and U2Y3 > U2Y5 and U1Y3 > UIYI, 01'
Y: ifU2Y7 > UzYs and UIYs > UIY7 and U2Y3 > U2Y5 and U|Y3 > UIYI, 01'
ifUng > U2Y7 and UIYg > Uij and U2Y3 > Ung and U1Y3 > UIYI, 01'

if U2Y7 > Ung and UIY'; > U1Y5 and U2Y3 > U2Y7 and U1Y3 > UIYI

Y5 ifUng > U2Y7 and U1Y5 > U|Y3 and U2Y5 > U2Y3 and [1ij > UIYI, or
ifU2Y7 > UzYs and U1Y5 > U1Y7 and UzYs > U2Y3 and U1Y5 > UIYI

Y7 ifU2Y7 > Ung and U1Y7 > U1Y5 and U2Y7 > U2Y3 and UIY7 > UIYI

 

\Yg ifUng > U2Y7 and UIY3 > UIYs and Ung > U2Y3 and U1Y3 > UIYI

I can now formally relate the dependent variable — the actor’s choice at each
decision node — to the explanatory variables, which enter in as factors affecting the
actor’s utility for each of the outcomes in the model. If the model is speciﬁed correctly,
at this point, I could precisely identify which outcomes would be reached in the 1

interaction based on each actor choosing the action that maximizes its utility. These

 

’7 Assuming the model is estimated freely with no restrictions on preferences.

151

outcomes could then be compared with the outcomes achieved in the data to see how well
the model ﬁts the data.

At this point, I could solve the game depicted in ﬁgure 5.2 using backward
induction, and the predicted outcome would be the subgarne perfect equilibrium (SPE)
outcome. Using a contingency table I could then compare the predicted outcomes of the
model, yhm, to the actual outcome y. However, without some random component entering
_ the data, I cannot conduct the broader range of hypotheses tests that are desirable from a
substantive perspective. For example, in this paper statistical tests of the theoretical
hypotheses are developed that should be tested in a multivariate model that includes
control variables for factors that are assumed not to be affecting the outcome in the
theoretical model. However, it would be a practical impossibility to build an
interpretable contingency table with all of the control variables in it. Also, models based
on probabilistic outcomes that generate standard errors are preferred in order to make
inferences to broader populations.

Thus, to transform the theoretical model into a statistical model for testing, in
ﬁgure 5.3, a component of uncertainty is added to the theoretical model that assures some
positive probability of each outcome occurring. I specify this uncertainty as private
information belonging to states regarding their value for sanctions at each step in the
sequence of moves. Other players and the analyst only know (or assume) the distribution
of the outcome payoffs. The distribution of errors for this analysis are assumed to be
independently and identically distributed MO, 62), therefore the choice probability

equations take the probit form.

152

1- Sender

   

U,( SucThreat) + 71’33

UT( SucThreat) + an
U, (BackDowrb + 7:35
UT(BackDowrb + an
(ls (SucSanc) + 7,57 U, (FailSanc) + 7’53
U,- (Sch + 7:" UT (FailSanc) + If”

Figure 5.3: Sanctions Threat Model with Uncertainty Concerning Utilities.

The Utilities shown in the ﬁgure represent each player’s true utilities, broken into their
observable Ui(-) and unobservable nij components.

The solution concept used here is game theoretic and based on random utility

assumptions. The link used is probit. The concept is analogous to that developed by

McKelvey and Palfrey (1995, 1996, 1998), known as logit quantal response equilibrium

(LQRE), except that the LQRE assumes the errors are distributed type I extreme-value,

thus using a logit link. The basic idea for both forms of this solution concept is that

“players employ best responses to each other, conditioned on the knowledge that each

will make mistakes according to some known (or assumed) distribution of errors”

(Signorino, 1999; p.282).

153

As noted above, it seems unlikely that the targets and senders of sanctions are able
to perfectly observe each other’s utilities for the different outcomes of their interaction,
and, further, it is also unlikely that analysts are able to perfectly specify the utilities of the
actors in the game. Thus, Signorino (2000) assumes that the true utility for an outcome
can be represented as having two components: one that is observable and one that is not.

In ﬁgure 5.3 the utility for each outcome is broken up into these two components
(observable and unobservable). For example, look at the utilities for the sender and target

for a successful threat of sanctions.

U 's( SucThreat) = U,( SucThreat) + ;r,,
U 'r( Suc Threat) = UT( SucThreat) + an

Where U; (SucThreat) , is the sender’s true utility for making a successful sanctions
threat, but what the target and the analyst observe is Us (SucThreat) . 7:53 and an

represent the components of private information. The sender has private information
regarding the value of 7:33 while to the target and the analyst this is viewed as a random
variable over which they only know the distribution. The inclusion of this stochastic
component captures the idea that “more often than not, the quantities we are interested in
will not be predictable in advance but, rather, will exhibit an inherent variation that
should be taken into account by the model” (Ross, 1993; p. 1).

Assuming that the payoff perturbations are independently and identically
distributed N(0,oz), the probabilities for each of the equilibrium outcomes in the game

depicted in ﬁgure 3 will follow directly from the action probabilities.

154

 

 

the
the
“’11
(ice

The

LCI p sq , pSucThreat , pBackDown , pSucSanc , and pFailSanc be the probabilities for the

different outcomes available in the theoretical model presented in ﬁgure 5.3. Because of
the assumption made regarding independent and identically distributed errors, the
probability of any given outcome is the product of the action probabilities along that path

(Signorino and Tarar, 2001). Therefore,

P.=P
Parmeazller
ewm=ee.
EWW=Baﬁﬁ
Paris” :8 5?}: Es

and, following the more general model depicted in ﬁgure 5.2,

PK=R
Pn=ee
Pz=eee

Pn=eaee
P K; = P2 P4 Pa P3
Using ﬁgure 5.3, I can derive the choice probabilities over the actions available to
the actors in the model by starting at the last decision by player 2 and working back up to
the ﬁrst decision. In the game depicted in the ﬁgure there are four decision nodes, which
will be progressively numbered from 1-4 as the players move from the ﬁrst to the last
decision. Thus, at information set i4, player 2 (the target) must choose between as, and a7.

The probabilities for actions as, and a7 at this information set are:

155

p, = pr[U;(r,)>U;(Y7)]
= PT[U2(Y3)+7[23 > U2(Y7)+7[27]
U2(Y3)-U2(Y7)

2 2
\[O-"u + 0‘27

and p7 = l-pg38.

=<D

Now, because player 1 (sender) is uncertain about player 2’s (target) payoffs for
Y7 and Y3, at the last information set (i4) he must assess the probability that player 2 will
choose a7 versus a3 and use that probability in his expected utility calculation for his
utility of choosing action a(,. The probability that player 1 will choose action a6 is
calculated as
p. = Pr[U.‘ (a.) > (1:02)]

= pr[p7U.‘o;>+p.U.‘o;)>U:(Y.>]
= PrlpxU. 09+ m7)+p.(U. new.) > U. 09%.]

U (Y)+P3U (Y)-U. (Y5)

2

= (D p7
JP701|7+P§C72+UM

and p5 = 1- p6. Next, I move to player 2’s choice at information set 2. Here, player 2 will
base her choice on a comparison of her expected utility for taking actions a; and a4. In
making this choice, player 2 must consider two things. First, she must consider her

expected utility for taking as based on her expectation of whether player 1 will take action

as or a6. But when making this decision, she must also consider that player 1 will base his

 

’8 0(0) is the standard Normal cumulative distribution function used with the Probit link function

(1): 3::— “fe exp[——Zz)dZ.

156

decision regarding as or 36 on his assessment of the probability that player 2 will take
actions a7 or as at information set 4.
p. = Pr[U;(a.> > U509]

= Pr[p.U;<Y.) + p.U; (as) > U.‘(Y.>]
= Pr[Ps(U2 (Y5) + ”25) + P6[P7(U2 (Y7) + ”27) + P8(U2 (Y8) + ”28” > U2 (Y3) + ”23]

P5U2 (Y5) + Paivaz (Y7) + P3U2 (Ysll - U2 (Y3)

2 2

= (I)
2 2 2 2 2 2
Jpso-Xzs + p6(p70-X27 + p8o-x")+ 0.323

As in the previous cases p; = 1- p4,

Finally, I come to player 1’s choice at the ﬁrst information set. Here, player 1 will
base his choice on a comparison of his expected utility for taking actions a1 and a2.
Similarly to player 2’s decision at node 2, player 1 must consider all subsequent actions
when consider which action to make at this decision node. In making this choice, player
1 must consider his expected utility for taking a; based on his expectation of whether
player 2 will take action a3 or a, whether player 1 will choose as or as and the probability
that player 2 will take actions a7 or as at information set 4.

p. = Pr[U:(a.> > U.‘(Y.)]
= Pr[p.U:(Y.> + p.U.°<a.) + p.U.‘(a.) > Uioo]
= Pr[p.(U. 03) + 7n.) + p.{p.<U. (19+ m.) +p.[p.(U. om 2r...) + mu, (1;) + m1} > U. ()1) +

= (D PsUr(K)+P4{P5Ur(K)+P6[P7U,(Y;)+p8U,(1;)]}—U'(Yl)+,;”

2 2 2 2 2 2 2 2 2 2
Jp3a-K'3 +p4 [0.3” +p6 (p7dﬂ.7 +p80-Klg )]+O'ﬂ”

Assuming that the actors have maximized their true utilities at each of the

decision nodes, the above probabilities represent the strategic probit choice probabilities

157

for the sanctions threat model presented in ﬁgure 5.3 and throughout this section. Notice
in particular that these equilibrium choice probabilities reﬂect the logic of both the
uncertainty and strategic nature of the decisions made by targets and senders of sanctions.

The ntunerators in the above equations represent the difference in the observed
expected utility of each player for the options associated with each of the outcomes.
Consider the depth of complexity required for the sender of sanctions making the initial
decision to threaten sanctions or accept the status quo. This is represented in the last
probability calculated above, p2. The sender has to compare his expected utility for
threatening with his utility for not threatening. However, the sender has to consider all of
the potential outcomes that could be reached if he decides to threaten and the probability
of achieving each of those outcomes. This is not a simple matter, as can be seen in the
above probability function. This set of equations highlights the fact that early on in a
dispute decision makers make decisions in a world of greater complexity and uncertainty
than they do at later stages of the dispute. As an actor moves down the tree we see that
decision makers are faced with less and less uncertainty in their choice probabilities
because they have fewer potential responses to their actions that need to be weighed.
Although in this model decision makers do not explicitly learn from the actions of other
players, they do reduce their uncertainty as they proceed through the dispute, which is a
type of learning.

This statistical attribute of the model captures what Nye (2003) refers to as “The
Narrowing Funnel of Choices.” (Nye 2003; 79; see ﬁgures 3—4 and 4-4). Nye presents
this concept as a general narrowing of available choices, so that looking back on the great

conﬂicts (as he does) we see that initially there were inﬁnite paths that could have been

158

taken. However, as the dispute proceeds, the available choices shrink with each
important step taken toward the conﬂict. Eventually, on the eve of war, there is very little
room for maneuver and options that were previously available to leaders are no longer
available. Of course none of this is deterministic, and the funnel can temporarily widen
and contract, but at each step the probability for war becomes greater. This process is
captured in the strategic probit model used here. During the last moves of the game, there
. are few potential responses that a decision maker must consider. At the early stages,
however, a decision maker has to consider many possible responses to his moves. Then
he must consider the response he would make to each of those responses and the response
the other player would make to each of his responses to her responses. But when the
funnel has contracted at the end of the game, the outcome from your move may be the last
one that needs to be considered, thus greatly simplifying your decision.

Another important element to consider is that the denominator of each probability
equation is a variance term, which reﬂects the amount of uncertainty regarding the
unobserved component of the true utilities. (Signorino and Tarar, 2001 :7) When 02 is
large relative to the observable utilities in the numerator, the choice probabilities at each
decision node will get closer and closer to being made randomly with a 50% probability
either way. When 0'2 is small, decision makers have more complete information and
decisions will be made with probabilities approaching 0 and 1. As 0'2 approaches 0, the
model is reduced to one of complete and perfect information and due to the assumption
that actors will maximize their utilities at each decision node; the outcomes can be
determined using subgame perfect equilibria concepts. Therefore, one of the most

fundamental differences between this model and traditional game theoretic models is that

159

 

Fig

the analyst is assumed to know only the distribution of the error terms, and can therefore,
only make probabilistic statements about the equilibrium choices.

Next, in ﬁgure 5.4 I show a general speciﬁcation of the utilities that the sender
and the target have for speciﬁc outcomes in the model, which are represented as a linear

function of the XB’s.

 

£170 X18 ﬂl8
P... X28 ”28

Figure 5.4: Specification of the Sender and Target’s Utilities in Terms of Regressors.

160

Notice that the model differentiates the sender and target’s utilities for outcomes like a
successful (or failed) threat of sanctions or a successful (or failed) implementation of
sanctions. Also notice, that the target’s utility for the status quo outcome is not speciﬁed
because it does not affect the choice probabilities later in the game.

Throughout the model, the sender and target’s utilities for successful sanctions are
both estimated with a constant. According to Signorino and Tarar (2000) this is not
necessary as long as variables different from those included in the utilities of the sender
and target for failed sanctions are used. Using the same variables in the utility equation
for capitulation [successful sanctions] as in the equation for war [unsuccessful sanctions]
creates an identiﬁcation problem (Signorino and Tarar, 2000). However, it is not
necessary to include these variables, and in other strategic probit models used by
Signorino (2000) and Leblang (2000) constants are used to represent one side of a
decision. Since using a constant is convention, and I ﬁnd no way to include a variable
that makes up one’s utility for failed sanctions and does not also affect ones utility for
successful sanctions, I simply estimate a constant for that side of the choice.

Next, the equilibrium outcome probabilities calculated above are used as the basis
for maximum likelihood estimation with respect to the B’s. Let y,,,, ,-= 1 if the dispute in
observation i results in a status quo outcome, and zero otherwise. Let ymmm ,-= 1 if the
dispute results in a successful threat by the sender, and zero otherwise. Let ygacwom ,- = 1
if the dispute ends in a back down by the sender, and zero otherwise. Let ySucsm, I: 1 if
the dispute ends in a successful sanction by the sender, and zero otherwise. Finally, let
ypauSanc, ,- = 1 if the dispute ends in a failed implementation of sanctions by the sender, and

zero otherwise. Now I can specify the log likelihood to be speciﬁed as:

161

1 [1L i y sq ,i In p sq ,i + ySucThreat,i 1n pSucThreatJ + yBackDown,i 1n pBackDown,i +

i=1 ySucSanc,i 1n pSucSanc,i + yFailSanc,i ln pFailSanc,i

According to Signorino and Tarar (2001), since one cannot generally estimate the
effects parameters (i.e., the B’s) and the variance parameter 0' individually, 0'2 is
normalized to one, meaning that the parameter estimates are estimates of the [3’s and o to
scale.

Now that I have the statistical model in which I have assured that there is some
positive probability associated with each outcome through the introduction of a speciﬁed
distribution of error on the part of the actors and the analyst, I can use the theoretical
model directly as a statistical model that can be estimated within a strategic probit
framework. Thus, the theoretical model and its empirical test are uniﬁed. In the next
section I describe the data that was collected for Operationalizing the variables in the
model.

Research Design: Operationalizing Concepts in the Theoretical Model

As mentioned earlier, a unique data set has been created for the analysis of the
formal model of threats of sanctions. Since 1987 the United States has been threatening
and imposing sanctions on countries that it feels are not giving their ﬁrll cooperation in
combating the international trafficking of illicit narcotics. It is interesting to note that this
policy, like many other sanctions policies in the United States, has come under heavy
criticism recently for its lack of observed success. In fact, there is a concerted effort
within Congress to put a halt to United States sanctions under this policy because many

are in agreement with the criticisms recently articulated by Sen. Kay Bailey Hutchison

162

(R-Tex.), who said, "The drug certiﬁcation ritual results in resentment and is
counterproductive” (Dewar, 2001).

Part of the problem with this reasoning is that it is based completely on the
observation of the successes and failures of the cases where this policy has actually been
implemented. In the 15 years since the policy was implemented back in 1987, there have
been very few clear successes where sanctions were imposed on a country and it
responded by reducing the amount of drugs trafﬁcking through its country.

In this paper I propose that, as in other cases of economic sanctions, the most
obvious evaluation of the policy may not be the best. Therefore, this sanctions policy
may be more successful than it appears because the majority of the successes are taking
place in response to the threat of the imposition of sanctions. Many of the countries that
will comply with sanctions would comply when threatened rather than wait for the actual
implementation of the sanctions. This should be particularly true for democracies that
face high audience costs for backing down. Therefore, judging the success of these
sanctions based on the success of implemented sanctions may exaggerate the likelihood
of failure, while simultaneously ignoring the successes achieved through deterrence.

In this section the general model of ﬁgure 5.1 will be translated into a model

speciﬁc to the data being analyzed. This “data speciﬁc” model is seen in ﬁgure 5.5.

163

em. om g... was

 

 

 

 

~Threat Cami ~Sanction Fa'led
y 0" ' “y Sarctions
Status Successfu Fa'led Successful
Quo Th’eat Threat Sardions

Figure 5.5: Drug Sanctions Model

As the ﬁrst step in this process, when the United States names a country, suspending half
its aid and threatening to withhold all aid, the outcome is equivalent to the sender making
a threat in the ﬁrst move. If the country is not placed on the list, no threat has been made
by the sender in the ﬁrst move.

At the second stage of the process, after sanctions have been threatened, the
United States asks for a certain type of behavior from the named countries. At this stage
the named country must decide whether to cooperate with the United States’ demands in
the h0pes that it will avoid sanctions, or refuse to cooperate and hope that the United
States will back down from its threat.

It is interesting to note Baldwin’s minimum requirements for a threat relationship
to exist. He provides two minimum conditions “(1) B must have some idea as to what A
wants him to do; and (2) B must perceive a difference between the way A will respond if
b complies and the way A will respond if B fails to comply” (Baldwin, 1971; p.76). It
can be argued that both of these conditions are met by the language of the United States
drug sanctions policy. However, Baldwin also notes that there “are circumstances in
which A might actually prefer to make vague demands on B” (Baldwin, 1971; p.76). The

United States has a general idea of what it wants, at least as far as the results, but not such

164

a good idea of how it should be achieved. In this way, the vagueness of the United States
demand for countries to cooperate in preventing the trafﬁcking of illicit narcotics is
strategically vague. Baldwin states “when A is uncertain as to what he wants B to do in
the future, he may want to be less precise in communicating his demands to B” (Baldwin,
1971; p.76).

Once a country is placed on the “Majors” list and the second step in the process
begins, the president must decide whether to certify each of the “named” countries.
Again, for purposes of comparison with the theoretical model, the “named and certiﬁed”
outcome is analogous to the sender threatening and the target cooperating in the second
move of the game.

If the United States decertiﬁes a country for non-compliance, it can still back
down in the face of a resolute target by granting a sanctions waiver under an exception
that allows the president to claim that sanctions would threaten the United States’ vital
national interests. This occurs when the president determines that it is in the United
States’ vital national interest to be “able to cooperate, provide foreign assistance or vote
for assistance from the multilateral development banks despite the country’s failure to
meet full narcotics certiﬁcation standards” (United States State Department; p.2). If the
president makes this determination, then the foreign assistance given to that country
remains unchanged.

If the president decides to deny certiﬁcation, and does not grant a waiver, the
repercussions are serious. Most categories of economic assistance to decertiﬁed countries
are cutoff. Speciﬁcally, most forms of aid provided under the FAA, the Arms Export

Control Act, and any ﬁnancing through the Export-Import Bank are immediately

165

terminated. In addition to these sanctions, the law requires the United States to vote
against any loans from multilateral development banks to decertiﬁed countries. This
denial of certiﬁcation without the granting of a waiver is analogous to the sender
imposing sanctions in its second move of the game.

Cooperating with the United States’ demands allows the country to avoid being
sanctioned but is not a costless action. In many countries the cost to the government of
_ standing up to powerful drug cartels is very high. In the theoretical portion of this paper,
it is argued that the costs to the government will depend critically on the institutional
arrangements that govern the selection of political leaders in that country. When
government leaders are beholden to small groups of elites for their tenure, opposing
powerful drug cartels will come at a great cost to leaders who seek to remain in office.
On the opposite end of the cost spectrum lie leaders in democratic countries who seek to
satisfy a broad coalition of citizens and are thus rewarded for successful public policies
increasing the welfare of typical citizens.

Since the leaders of threatened countries face costs for either decision,
cooperating or not cooperating with the United States’ demands, it is not unreasonable to
assume that these leaders perform a type of expected utility calculation for each of these
decisions. The most immediate costs to these leaders are, on the one hand, the loss of
United States foreign aid and a lessened potential of receiving aid from international
lending agencies and, on the other hand, the costs associated with alienating leaders of

powerful drug cartels in their home country”.

 

’9 For a description of how these decision have played out with respect to one speciﬁc country, see the

Laos case study in Appendix C of this dissertation.

166

Description of Data

Throughout the lS-year history of amendment 490 to the Foreign Assistance Act,
sanctions have been imposed on a number of occasions. 33 different countries have been
named at one time or another as being a major producer or trafficker in illicit narcotics.
The highest number of countries named in any one year was 31 in both 1996 and 1997.
The lowest ntunber of named countries was 24 occurring in each of the ﬁrst 4 years of the
policy’s existence. Eight separate countries have been subject to sanctions during this
time for 53 sanctioned country years. Thirteen different countries have received national
interest waivers for a total of 41 country years, and 29 different countries have been
named and certiﬁed as cooperating with United States efforts.

Of the 8 countries that have been subject to United States sanctions 7 were
autocracies and only one, Colombia, was a democracy as measured by the polity index
used to measure democracy in this dissertation (Colombia received a democracy score of
7 out of 10). In this case, the United States sanctioned Colombia for 2 years, in 1996 and

1997, before backing down and granting a national security waiver the following year.

Dependent Variable: Outcome
The dependent variable for this portion of the analysis is coded to take on 5 different
values — one value for each of the outcomes available in the theoretical model.
Outcome 1: Sender makes no threat. This corresponds to not being named as a major
drug trafﬁcking country.

The logic behind this coding is as follows: It is clear that if the United States does

not place the target country on the “majors” list, no credible threat of sanctions has

167

occurred. If the country is not placed on the “majors list,’ there is no provision for adding
the country to the list of sanctioned countries at the time that the drug sanctions are
imposed.

Outcome 2: Target cooperates at the threat stage. This corresponds to the target taking
steps to counter illegal narcotics trafﬁcking in/through its boarders and results in the
target being certiﬁed as cooperating with the United States.

The logic behind this coding is as follows: What we observe is the United States
making a threat, by placing the target on the majors list and temporarily suspending half
of its United States foreign aid, and then four months later certifying that the target is in
compliance with the demands made in the threat (and returning all of the target’s aid). It
seems logical to assume that the target did in fact cooperate with the United States threat
to impose sanctions, or the target would not have received certiﬁcation as a state in full
compliance.

What differentiates this coding from outcome 3, where the sender is deemed to
have backed down from its threat, is that in outcome 2 the sender acknowledges that the
target is cooperating.

Outcome 3: Sender backs down after initial threat of sanctions. This corresponds to the
sender deciding not to sanction a potential target after initially making a claim that the
target is a major trafﬁcker in illegal narcotics and is not cooperating with the United
States to curb this traffic.

The logic behind this coding is as follows: What we observe is the United States
threatening the target state, and then later decertifying the target, but granting a national

security waiver to avoid the imposition of the threatened sanctions. If the United States

168

were in fact happy with the cooperation they received from the target, they could have
certiﬁed the target as cooperating and no sanctions would have been imposed. However,
by decertifying the target, the United States is acknowledging that they did not receive the
cooperation they were trying to gain. Even in light of this lack of cooperation, the United
States still does not impose the threatened sanctions. This can clearly be seen as a failure
to carry out the threatened action even though the sender’s actions make it clear that the
target did not take the actions demanded.

Outcome 4: Target complies after being sanctioned. This corresponds to the target of
sanctions c00perating with the sender’s demands after a period or more of being
sanctioned. This outcome is observed when the sender removes the target from its list of
major narcotics traffickers, or certiﬁes the state as cooperating to reduce trafﬁc through
its boarders.

As discussed in earlier chapters, there are many ways to think about success with
regard to uses of sanctions. One of the clearest operational deﬁnitions of success is
whether the policy objective was achieved. There are two potential objectives of this
policy. The primary objective of this policy is that the narcotics problem in the target
state is reduced to acceptable thresholds as outlined in the United States Department of
State Document entitled “Legislative Basis for the INCSR.” This would be an ideal
outcome, but is not necessary for success. Some countries may have drug problems that
are too severe to be eradicated completely. In this case, a second way for this policy to
achieve success is if, at a minimtun, the target country shows increased effort in

combating the problem.

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Therefore, the logic behind the coding of success is as follows: What we observe
is the United States, after imposing sanctions on the target in the previous year, taking
either of two actions in the following year. First, the United States could remove the
target completely from the “majors list,” thus ending the threat of sanctions completely.
Seeing the United States take this action is a sign that they are no longer concerned with
the amount of illegal drug trafﬁc through or from that country. Second, the United‘States
could leave the target on the majors list, indicating that the target country still has a
problem with the trafﬁcking of drugs through or ﬁom its country, but that the target is
now cooperating with the United States to minimize this trafﬁc. When this action occurs
on the part of the sender, it is not necessary that there was an actual decrease in the
amount of drug trafﬁc through or from the target. The sender is not saying that the target
no longer has a drug trafficking problem, only that the target is now cooperating to help
reduce that traffic.

The ﬁrst scenario mentioned above could also be an indicator of defeat for the
sender. In order to consider the United States’ removal of the state from the majors list a
success there should also be a clear drop off in the amount of drug trafﬁc or production
observed in the target country. If the United States removes the country from the list
completely and there is no noticeable drop in drug traffic or production in that country it
should be counted as a failure where the United States simply gives up.

Under this coding scheme, there are four cases that I identify as successful uses of
this policy. Each of these cases is listed below. A brief description of events supporting
the coding of each of the success cases of the drug sanctions policy is included in

Appendix C: Justiﬁcation for coding of success cases. The case study for Laos is clearly

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the most developed and will serve as a template for proposed future case studies of all
countries affected by this policy. The rest of the cases are superﬁcially developed at this
point and are only intended to provide enough information to support the minimum
justiﬁcation for having been coded as a success.

Syria 1998 — Removed entirely from majors list after being sanctioned
the year before.

Iran 1999 — Removed entirely from majors list after being sanctioned
the year before.

Laos 1990 - Went from sanctioned to named and certiﬁed

Panama 1990 — Went from sanctioned to named and certiﬁed

Outcome 5: The target does not comply with the demands of the sender even after being
sanctioned. This results in continued sanctions in the following year.

The logic behind this coding is as follows: If the sanctions continue in the year
after the initial sanctions were imposed, then the country can not be said to have
cooperated. This is a clear indication that the sanctions did not work to change the
behavior of the target during the previous year.

Coding the dependent variable in this way implies that success or failure of threats
and implemented sanctions can be determined by the actions of the sender. Another way
to code the outcome variable would have been to look at the amount of narcotics seizures
in the countries threatened with or subject to sanctions and see if it decreased in the years
following the threat or the implementation of sanctions. I chose not to use this
operationalization because I do not think it captures the concept of success implied by the

goals of policy. The United States is not asking the targeted states to eliminate the

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trafﬁcking of illicit narcotics through or from their states. In some cases this might be a
virtually impossible task. What the United States is asking is that the targeted state’s
government make efforts to cooperate with the United States in combating the existing
drug trade from or through their state. Because of this distinction, just looking at the
actual amount of drug traffic is not a fair test of the success of the policy.
Independent Variables

As shown in the deductive model that this empirical analysis tests, there are three
essential elements that have to captured to understand the threat and use of sanctions.
First, the expected cost of sanctions to the sender and target must be considered. Second,
the value of the policy at dispute to the sender and target has to be considered. And third,
the political value of the relationship between the sender and target in political terms is
considered. Along with this, there are also domestic factors of importance discussed
earlier like the stability of the target regime and the type of domestic institutions in the
target government. Of course, in order to focus on theoretically important concepts, there
are certain elements of potential importance that may be excluded from the game
theoretic model with the assumption of ceteris paribus. These elements cannot be
assumed constant as in the deductive model, so they must be included in the inductive
model as empirical control variables.
Building the Strategic Probit Model

The strategic probit model analyzed in this section is designed to replicate the
game theoretic model of the previous chapter. Thus, variables affecting sender and target
utilities at each of the various stages of the game will be entered into the model. The

following is a brief outline of the concepts included in the model and the variables used

172

for their operationalization. I will then discuss each of these variables in slightly greater
detail before moving on to the empirical analysis.

Expected Costs of Sanctions to Sender and Target: Economic Value of the
Relationship

0 Target Aid % of GNI (TAPGNI)

0 Target Trade % of GDP (T'I'PGDP)

0 Trade between sender and target (LTTST)

Value of the Policy at Dispute
0 Target Drug Problem (TDT)
0 Drug Seizures Plus Drug Production
0 Target Drug Problem * Target Democracy Level (LTDPTDL)

Domestic Factors Affecting Decision to Comply with Sanctions Demand
0 Target Democracy Level (DEM7)
0 Control Variable for Target Regime Stability: Target Internal Conﬂict (TIC)

Political Value of the Relationship
0 Regime similarity: Target Democracy Level (DEM7)

Additional Control Variables

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Variable Min Mean Max. Variance Std. Dev.
TAPGNI 0 9.72 233.59 243.87 15.6162
TTPGDP 1.41 78.29 406.75 2385.18 48.8383
LTTST 0 5.86 12.81 7.07 2.6596
TDT 0 0.85 43.04 15.88 3.9844
DRUGSEIZ 0 0.57 28.29 6.0674 2.4632
DRUGPROD 0 0.48 32.47 7.6216 2.7607
DEM7 0 0.41 l 0.24 0.4913
LTDPTDL 0 0.39 43.041 7.76 2.7848
TIC 0 0.17 l 0.14 0.3748
SCAMERIC 0 0.21 l 0.17 0.4085

 

0 Regional hegemony: South/Central America Dummy (SCAMERIC)

Table 5.1: Descriptive Statistics for Variables Appearing in Empirical Analysis

Expected Costs of Sanctions to Sender and Target: Economic Value of the
Relationship

173

 

Because this model evaluates the effectiveness of threats of sanctions, the
measures used here do not attempt to explicitly measure the amount of economic costs
that were imposed as a result of sanctions impositions. Rather, by measuring existing
levels of the economic relationship between potential senders and targets the emphasis is
shiﬁed to the ability to impose economic costs through sanctions. This is similar to the
methodology used by van Bergeijk (1995) who looks at existing levels of trade linkage
between senders and targets of implemented sanctions rather than using the more
traditional Hufbauer, Schott, and Elliot variable measuring estimated costs of sanctions
that were imposed.

Measures of Target Costs:
Measure 1: Target Aid as a percent of Gross National Income

One measure for the cost of sanctions is represented by the level of aid received
by the target as a percent of GNI. This is the most direct measure of cost to the target,
because the sanctions are speciﬁcally aimed at removing all foreign aid to decertiﬁed
countries. The amount of aid provided speciﬁcally from the United States could also
have been used here, but it would not have captured the full effect of the sanctions.
When the United States sanctions a country under this policy they not only cut off United
States aid but also utilize their inﬂuence with international organizations to attempt to
block all potential sources of international aid to the country. Also, countries that rely on
a large amount of foreign aid will suffer additional costs from the sanctions due to
increased difficulty in receiving loans and higher ﬁnance rates given they are viewed as
less reliable.

Measure 2: Target Trade with Sender as a Percent of Target Gross Domestic
Product

174

For many of the same reasons stated above countries that have a higher amount of
Trade as a percent of GDP are expected to suffer greater costs when they are subject to
the United States’ sanctions policy, and hence be more likely to comply. Typically, the
trade linkage between the sender and target would be of most interest here. Van Bergeijk
(1992) shows that when the bilateral trade ﬂow between the sender and the target, as a
percentage of the target’s GDP, is less than 1% the success rate of sanctions decreases
dramatically (from 38% to 19%). However, since United States drug sanctions are
directed at cutting off aid and ﬁnance as opposed to trade, a broader measure is needed.
Financial sanctions such as those imposed by this policy, which restricts aid and
access to loans from international banks, are asserted by many to have implications for
trade and ﬁnance in the targeted nation that go beyond the actual loss of the sanctioned
aid. Weiss and Selden (1999) elaborate on the potential effects of ﬁnancial sanctions,
which demonstrates their ability to cause harm to targeted nations.
“Financial sanctions can be particularly eﬁective precisely
because capital is the one commodity for which most nations
cannot substitute domestically. By blocking the ﬂow of capital
from the cheaper lenders, through the World Bank and the IMF
the cost of capital goes up. That hurts the elite of the target
country, who then have to make some hard choices about
where to cut spending—or accede to the wishes of the global
majority” (Weiss and Selden, 1999: 55)
Furthermore, Hufbauer, Schott, and Elliot (1995) explain how the “interruption of
commercial ﬁnance will usually require the target country to pay a higher interest rate to
alternative creditors. . .In addition, when a poor country is the target, the grant component

of official ﬁnancing may provide further leverage (Hufbauer, Schott, and Elliot, 1985:

28).

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Measures of Sender Costs:
Trade Between Sender and Target

The inﬂuence of the trade linkage to the sender must be calculated in a different
manner as the importance of the linkage to the target. For the sender, who is proposing to
take actions jeopardizing the existing trade relationship with the target nation, it is the
amount of bilateral trade as a percentage of its total economy that is relevant. In this
study, since the sender is always the United States, total trade between the United States
and the proposed target will be used.
Data for total United States trade with all other countries for the years 1986-1996 were
obtained from Kristian Gleditsch’s IMF Trade data, available at:
http:/g-gleditschsocsci.gla.ac.uy.

For the years 1997-1999 the data were obtained from the University of Virginia

Library Geospatial & Statistical Data Center through their web page at:

http://ﬁsher.lib.virginia.edu/.

 

The UVA data is available from 1989-1999, so there were several years that
overlapped with the Gleditsch IMF data. This allowed for a correlation test to be run on
the overlapping years to ensure the data was indeed measuring the same concept. The
correlation coefficient conﬁrmed that the data are highly correlated. The Pearson
correlation coefﬁcient is .99 (nearly perfect) with a signiﬁcance level better than .001.
Value of the Policy at Dipute
Target Drug Problem: Drug Seizures Plus Production

Indices are created for drug seizures and drug production. These indices are then
added together to give the total drug problem in the target country. Essentially, the

United States is concerned with high levels of both production and trafficking.

Sometimes these are separate issues and at other times they are combined. For example,

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Panama is a large trafﬁcking state but is not a major producer of narcotics, while
Colombia is high in both areas. Since the United States Drug policy explicitly mentions
both of these areas as concerns that can result in a state being placed on the “major” drug
producing and trafficking list, I include both measures in this analysis. By adding these
two areas together, I capture the “double threat” that nations like Colombia represent
when they are involved heavily in both trafﬁcking and production. States that are ‘
extremely high on one or the other of the indices (For example Brazil in 1986 was a
major trafﬁcking state but not a major producing state) or at a mid level on both (Laos has
ﬁt this description in many years due to its production and trafficking in opiates) can also
end up representing a large drug threat to the United States.

Drug Seizures Index
Higher amounts of drug traffic make the value of the policy higher for both the

sender and the target. For the sender it is clear that higher levels of traffic indicate a
higher level of the activity that it is trying to discourage. For the target, increased levels
of trafﬁc represent a greater extent to the drug problem in its country, hence a potentially
greater cost both politically and economically for ﬁghting the problem.

Following the practice of the United Nations International Drug Control Program,
and the International Narcotics Control Strategy Board, seizures of illicit narcotics are
used as a measure of drug traffic in a country. This data is recorded for all countries and
standardized by converting all measures to Kilograms. The primary source of seizure
data ﬁ'om 1986-1998 is the Annual Reports Questionnaire (ARQ) administered by the

UNDCP. The UNDCP monitors the quality of this data and reports that “The following

177

additional sources of information were used in cases where reporting by ARQ was
incomplete or lacking:

(a) International Criminal Police Organization (ICPO/Interpol)

(b) Reports provided separately by governments or other official sources

(0) International narcotics Control Board

(d) World Customs Organization

(e) UNDCP ﬁeld ofﬁces” GINDCP, 1996; p.3)

Much of the data that is available from the United Nations is also available from the
’United States’ International Narcotics Control Strategy Report (1N CSR). However, the
preferred source for this study was the United Nations. The exception is that when
Organization of American States (OAS) data was available and it differed from the UN
data, OAS data was used (Statistical Summary on Drugs, 2001). I made this exception
because the OAS has extensive drug seizure and production data, and since it is closer to
the source I assumed that it would be more accurate than the UN data. In some cases,
when there was a large discrepancy between the UN and OAS data, I took the largest
value based on the assumption that the lower value could be do to a tendency to
underreport. The last source of data was always the INCSR. Since one of the goals of
this research is to investigate the use of United States drug sanctions, I must be
particularly sensitive to the fact that there could be a tendency for the United States to
overestimate production and trafﬁcking ﬁgures for those states that it imposes sanctions
against. However, when UN data is not available and United States data is available, the

United States data is used based on the principle that it is the best available data. At the

178

time of this study access to UN data was only available through 1998, so all data from
1999 and 2000 come from United States estimates.

Generally speaking, high amounts of seizures can be treated as a sufficient
condition to indicate a high level of drug trafﬁc through a country. It is not possible to
seize a high level of narcotics if there is not a high level transiting the border. However,
high levels of seizures should not be treated as a necessary condition for high levels of
drug traffic. Depending on the effort of the country’s interdiction program, low levels of
seizures do not necessitate low amounts of trafﬁc. The United Nations International Drug
Control Program, which is the source for the data on seizures used in this study, provides
a cautionary note on this subject. First, they state in the text “Seizure data are used as an
indicator for trafficking” (UN DCP, 1996; p.5). A cautionary note immediately follows
regarding the interpretation of the data, “However, such information has to be interpreted
with caution since seizures may also indicate efforts in law enforcement” (UNDCP, 1996;
p.5). A later publication by the UNODCCP (World Drug Report 2000) includes a more
detailed discussion of why seizure data is considered the best measure of underlying drug
trafficking activity. The basis of this argument in favor of using seizure data is that if
“seizures are a good indicator for trafﬁcking trends, then seizures should be highly
correlated with both supply and demand indicators” (UN ODCCP, 2000; p. 36). And, in
fact they are. “The correlation coefficient for opium production and heroin seizures and
the correlation coefﬁcient for coca leaf production and cocaine seizures over the 1980-
1998 period were found to be close to .95” (UNODCCP, 2000; p. 36). Also, in the
United States, “cannabis herb seizures and cannabis use among high-school students were

found to correlate strongly (Correlation coefficient of r=0.96 over the 1978-1998 period)”

179

(UN ODCCP, 2000; p. 36). In interpreting the results based on this data, then, we can
have a great deal of conﬁdence that states with high seizures are also those with a large
drug trafficking problem. At the low seizure end of the scale we cannot have as much
conﬁdence. Those countries with small seizure levels are more likely to also be those
states without a major drug trafficking problem, but this may also be an indicator of a
lack of effort on the part of the state’s interdiction program. However, the available

. correlation evidence provides strong support that seizures are measuring drug trafﬁc.
Computing Drug Seizure Index: For this index, seizure data are grouped into three broad
categories:

Cocaine seizures

Opium seizures

Cannabis seizures

Total cocaine seizures are calculated by adding the quantities of coca leaves, coca paste,
and cocaine (hcl) to get the total amount of cocaine seized“). Total opium seizures are
calculated by adding quantities of heroin, morphine and opium seized to get the total
amount of opiates seized. Total cannabis seizures are calculated by adding the quantities
of cannabis and cannabis resin to get the total amount of cannabis seized.

Next, following the procedure used to construct the correlates of war capabilities
index (Singer et. a1, 1972), a drug trafficking capabilities index is created. The total
amount of world seizures per year in each of the three categories is computed. Then each
country’s yearly percent of the world seizures in that area is calculated. Next, the

categories are added together and divided by three to give a total drug trafﬁcking threat

score. Using this procedure avoids having to calculate dose equivalents for each of the

180

types of drugs. For example, accounting for 20% of the worlds trafﬁc of opium products
under this operationalization is equal to accounting for 20% of the world’s traffic of
cocaine products. Simply adding together the quantity of each drug being trafficked
would be incorrect because 1 Kg of opium products represent a more serious threat than 1
Kg of cannabis products. But, as in the correlates of war capabilities index, by
standardizing the quantities as percentages of the total world quantity adding the
percentages produces a valid index. Two separate indices are created in this way: one to
measure the relative amount of illicit narcotics traffic through a country and one to
represent the relative amount of drug production in a country. In the next section I will
discuss the creation of the drug production index.

Extent of drug production
As was the case with drug seizures, drug production data is gathered from a

variety of sources. These sources included the International Narcotics Control Strategy
Report (various years, 1988-2002), The United Nations International Drug Program’s
International Narcotics Control Board Report (various years, 1988-2002), OAS drug data
from the Inter-American Observatory on Drugs (available online at:
www.cicad.oas.org/oid/), National Drug Control Strategy Report (2002).

The amount of illegal drug production is measured in a similar way as trafﬁcking.
First, three broad categories are created; cannabis, coca, and opium products. Within
each of these categories two types of production levels are measured; cultivation and
production. This produces 6 categories.

Opitun cultivation
Opium production

 

’0 See appendix on computing estimates of cocaine seizures and production

181

Coca cultivation
Coca production
Cannabis cultivation
Cannabis production
As with the trafﬁcking index, the total amount of world production per year in each of the
six categories is computed. Then each country’s yearly percent of the world production
in that area is calculated. Next, the categories are added together and divided by six to
give a total drug production threat score.
A Note on Converting to Doses

A second way to standardize these measures would be to convert the seizures and
production data into doses or standardized units. The United Nations Office for Drug
Control and Crime Prevention (UN ODCCP) provide a guideline for transforming
quantities of each drug into standard units. “The units are assumed to reﬂect a typical
street dose at street purity” (Global Illicit Drug Trends, 2001; p. 87). The conversion is as
follows:
1 unit of cocaine = 100mg
1 unit of heroin or morphine = 100mg
1 unit of cannabis herb = 500mg
1 unit of cannabis resin = 135mg

This conversion was performed, but the analysis in this project relies on the ﬁrst
measure for creating the drug problem index. The reason for preferring the ﬁrst method
is that it makes better use of the available seizure data. For example, the seizure data
includes seizures of coca leaves, coca paste, and cocaine (HCL), the last of which has a
dose equivalent of 100mg. Unless coca leaves and coca paste are converted to cocaine,

they cannot be converted at the standardized rate of 100mg = 1 dose. However these

conversions rates can differ by year and by region, depending on the potency of the leaves

182

and the reﬁnement practices used for conversion from leaf to street dose. Therefore the
seizure data on coca leaves and paste would have to be excluded or conversion rates
would have to be estimated. Since much of the traffic in illegal narcotics takes place in
unreﬁned goods, this would result in losing a good deal of information. The ﬁrst measure
avoids this problem by simply adding up the total amount of each of these illicit narcotics
seized, ﬁnding the average, and then determining how much traffic in that drug went
through a particular country relative to the average. By adding up the percent of the
averages for each country, for each drug, we can determine the relative amount of trafﬁc
in these three areas (opium, coca, and cannabis) per country.

Target Drug Problem * Target Democracy
The democracy-autocracy dichotomous polity indicator (described below) was

multiplied by the amount of drug seizures in each of the targets. This allows an empirical
test of whether democracies are more willing to cooperate with the United States when

faced with a large drug problem than are non-democracies.

Domestic factors affecting decision to comply with sanctions demand

Target Democracy Level
Democracy is measured for this study using the latest version of the polity IV

dataset available at www.cidcm.umd.edupolity/index.htrnl. For this analysis the indicator
for the level of democracy is dichotomized so that democracy levels greater than 7 out of
a possible 10 are coded as democracies and those less than 7 are coded as non-
democracies. The following excerpts from the Polity IV Dataset Users’ Manual (2000)

brieﬂy explain how the measures of democracy and autocracy are generated. For greater

183

detail on how the polity project measures democracy the reader is referred to the original
source listed in the bibliography (Jaggers and Gurr, 1995) or the web page listed above.

Polity IV’s Meﬁrement of lnstiﬁtionglized Democracy;

“Democracy is conceived as three essential, interdependent elements. One is the
presence of institutions and procedures through which citizens can express effective
preferences about alternative policies and leaders. Second is the existence of
institutionalized constraints on the exercise of power by the executive. Third is the
guarantee of civil liberties to all citizens in their daily lives and in acts of political
participation. Other aspects of plural democracy, such as the rule of law, systems of
checks and balances, freedom of the press, and so on are means to, or speciﬁc
manifestations of, these general principles. We do not include coded data on civil
liberties.

The Democracy indicator is an additive eleven-point scale (0-10). The operational
indicator of democracy is derived from codings of the competitiveness of political
participation (variable 2.6), the openness and competitiveness of executive recruitment
(variables 2.3 and 2.2), and constraints on the chief executive (variable 2.4)”

Target Internal Conﬂict
As many previous empirical studies have found that unstable target governments

are more likely to give in to sanctions, I include a control measure in the model for the
stability of the target government. To operationalize instability I use data on armed
conﬂicts from Gleditsch, Wallensteen, Sollenberg and Strand (2002). If a country is
experiencing internal armed conﬂict it is coded 1 and is presumed to have a lesser degree
of internal stability, otherwise it is coded 0.

The operational deﬁnition of an armed conﬂict in the data set used is “An armed
conﬂict is a contested incompatibility that concerns government and/or territory where
the use of armed force between two parties, of which at least one is the government of a
state, results in at least 25 battle-related deaths” (Strand, Wilhelmsen, and Gleditsch,
2002, p.2). One advantage of this data is the relatively low battle related death threshold
adopted which allows for the inclusion of more conﬂicts while still being “high enough

for the violence to represent a politically signiﬁcant event” (Gleditsch, Wallensteen,

184

Sollenberg and Strand 2002; p. 617). In this data set, internal conﬂict is deﬁned as
conﬂict within a country between a government and one or more opposition groups, with
no interference from other countries.

Additional Control Variable
South/Central America Dummy: This is a simple dichotomous indicator with

countries located in South and Central America coded 1 and all other countries coded 0.
Results

In this section, before proceeding to the results of the strategic probit analysis, two
cross tabulations of the data are presented showing the difference in the United States’
treatment of democracies versus non-democracies under its drug sanctions policy. The
following cross tabulations show the effect of democratic regimes in the target on the
likelihood of achieving compliance at the threat stage of sanctions compared to after

sanctions have been imposed.

 

 

a
Threat Compliance - Stage One Certiﬁcation Status

 

 

 

 

Compliance Non- Compliance
(Certiﬁed) (Decertiﬁed) Total

DDEM7 Non-Democracy Count 133 86 219
Row % 60.7% 39.3% 100.0%

Democracy Count 165 16 181

Row % 91.2% 8.8% 100.0%

Total Count 298 1 02 400
Row % 74.5% 25.5% 100.0%

 

Table 5.2: Target Compliance with Sanctions Threat
In table 5.2, a fairly even split between democracies and non-democracies can be
seen as far as being named and threatened with sanctions as a “Major” drug trafﬁcking

country (non-democracies being threatened about 55% of the time -- 219 of the 400

185

 

cases). This indicates that drugs are not a problem speciﬁc to either type of regime - or at
least the United States does not perceive it to be that way. However, democracies are
signiﬁcantly more likely to be reported to be cooperating with the United States and
receive certiﬁcation after they have been named. An astonishing 91.2% of the time that
democracies are named, they receive certiﬁcation as cooperating in the ﬁght against
international drug trafﬁc. This implies that even though these democracies ﬁt the
deﬁnition of a “Major” drug trafficking country, they are willing to cooperate with the
threats/demands of the United States in combating the problem. By comparison, non-
democratic countries are certiﬁed as cooperating only about 60% of the time. Although
the main support for the hypotheses comes from the strategic probit model, this provides
some additional support for Hypothesis 1, which proposes that sanctions threatened
against democracies, will be more successful than those threatened against other regime
types.

Non-Democracies have clearly been less likely to cooperate with the United
States’ demands once they have been threatened with sanctions. This increases the
probability that the confrontation will escalate to the next stage in the sanctions game
when the target is a non-democracy. At the next stage the sender nation can give in after
denouncing the target as not cooperating in ﬁghting international drug traffic, or it can
follow through with its threat and impose sanctions.

Looking at ﬁrst stage compliance (again, from table 5.2), regardless of the polity
of the target, shows that about 75% of the time (298 out of 400) the United States

receives cooperation and certiﬁes the countries that it named. Decertiﬁcation includes

186

cases where the country was decertiﬁed and then granted a waiver for national interests
together with cases where a country was decertiﬁed and eventually sanctioned.
Restricting the analysis to just the 102 cases where a country was not certiﬁed,
shows the probability of the United States backing down and granting a national security
waiver after decertiﬁcation. Table 5.3 shows that disregarding the polity of the target, the

United States does not have a very good track record of carrying out its threat at this

 

 

 

 

 

. stage.
Waiver Status
Sanctions Sanctions
Waived Imposed Total

DDEM7 Non-Democracy Count 29 57 86

Row % 33.7% 66.3% 100.0%

Democracy Count 14 2 16

Row % 87.5% 12.5% 100.0%

Total Count 43 59 1 02

Row % 42.2% 57.8% 100.0%

Table 5.3: Does Sender Carry Out Threat

Only a little more than half of the time (57.8%) that the United States identiﬁes a
country as not cooperating in the ﬁght against international narcotics trafficking, does it
follow through and impose sanctions. In other words, in stage 1, when the United States
sends the message that the target state must cooperate or be sanctioned, there is some
reason to believe that it may be bluffing. What is of great interest to us here is that the
United States appears to be much more likely to carry out its threat when dealing with an

autocracy than when it is dealing with a democracy.

187

Since 1988, 16 democracies were threatened with sanctions and failed to comply.
During the same period 86 non-democracies were threatened and failed to comply. Based
on the target’s regime type large disparities in the United States’ commitment to carry out
its threatened sanctions can be seen. After decertifying the country as not cooperating,
sanctions were imposed against democracies only 12.5% of the time, while 66% of the
time they were imposed against non—democracies. This shows a very large discrepancy in
the way the United States is administering its sanctions policy against democracies and
non-democracies“. The United States seems to not be afraid to test the waters and
threaten their democratic counterparts, but when push comes to shove the United States is
likely to back down on its threat of imposing potentially harmful sanctions against
democracies.

However, it would be incorrect to assume that this means the United States is
unable to effectively use sanctions against fellow democracies. In the previous stage of
the interaction we saw that democracies are much more likely to give in at the mere threat
of sanctions than are non-democracies. So the United States has good reason to test the
waters and threaten democracies. Many times this leads to exactly the behavior the wish
for, without ever having to impose harmful sanctions. However, when facing a tough
democracy that does not comply after the initial threat the United States appears to prefer
to back down rather than impose damaging sanctions.

The ﬁnal point to note is that as hypothesized (HI), the success rate of this policy

has been dramatically higher at the threat stage than at the implementation stage. At the

 

" These ﬁnding are replicated in the strategic probit model, where I see that even controlling for

multiple other factors the United States is still signiﬁcantly more likely (z < .01 level) to back down against

188

threat stage approximately 75% of the cases were successful (298 out of 400), while at the
implementation stage only 4 of the 59 implemented sanctions were successful

(approximately 6.7%)“.

 

democracies.

’2 See Appendix C for a brief description of the four successes.

189

Results of the Strategic Probit Model
Performance of the Model

Table 5.4: Cases Correctly Predicted

 

 

 

 

 

 

 

 

 

Predicted
Y1 Y3 Y5 Y7 Y8 Total

Y1 101 l 28 0 0 0 1039

Y3 128 84 9 0 0 221
Observed Y5 12 19 l 0 0 32

Y7 3 0 0 1 0 4

Y8 l8 9 0 0 O 27

Total 1 172 140 10 1 0 1323

 

 

 

 

 

 

 

 

Cases Correctly Predicted = 82.918%. Null model predicting modal category = 78.534%

The model ﬁt statistics in table 5.4 show an improvement over what one could do
simply by predicting the modal category, that the United States does not threaten
sanctions. 78.5% of the time in the data, this is what occurs, so it would not be a bad
guess just to predict that the United States will not make a threat. The other 21.5% of the
time the United States threatens sanctions. Imposing a threat in 21.5% of the nation years
under observation represents a signiﬁcant amount of activity by the United States This is
particularly true when one considers the nature of the sanctions, which indict the target
country as a major drug producing or trafficking country and threatens them with
ﬁnancial sanctions if they do not take appropriate steps to mitigate the problem. The
model does a very good job of prediction outcomes at this stage of the process. Of the
1039 cases of no threat the model correctly predicts 97.3% or 1011. Once the sanctions
have been threatened, 16.7% (221 cases) of the time the United States gains compliance
with the threat. The model correctly predicts 38% of these cases of target compliance

whereas a null model would predict none of them. In the data the sender backs down

190

 

about 2.5% of the time and the model correctly predicts only .03% of these cases. In
37.5% of the cases where the sender threatens, only to have to back down when the target
calls its bluff, the model predicted that the sender would never make the threat in the ﬁrst
place and in the rest of the cases the model incorrectly predicts that the sender will have
to back down at the mere threat of sanctions. Of course rare events are more difﬁcult to
predict and true to form the model has difﬁculty predicting the relatively few cases of
implemented sanctions that do occur. The model only predicts .03% of the sanctions
years that occur in the data, although it impressively manages to predict 25% of the
sanctions successes correctly.

Interpreting the results of the strategic probit model:

The sender and target’s utilities for each of the outcomes in the model will be
affected by several elements in their utility functions. As was seen in the deductive
model, these factors will have on inﬂuence on the outcomes actors would prefer at each
stage of the model. The following sections will provide an interpretation of the
coefficients on each element of the actors’ utility ﬁrnctions for each outcome as seen in

table 5.5.

191

Table 5.5: Strategic Probit Estimates: 1-2-1-2 Model with Private Information

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Outcomes
Variable No Target Backs Sender Successful
Threat b Down (complies Backs Down Sanction
Sender with threat)
Sender’s Utilities Normalized = 0
Target Democracy (DEM7) 0.0621 1.2360“
Target Drug Threat or ~0.3241 "* .0740‘"
Problem (TDT)
Logged Total Trade between 0.6898*" 1.4689‘"
Sender and Tame; (LTTST)
South/Central America -3.1290" * -3.0401* "
Dummy
Target lntemal Conﬂict .3891 -2.5636“"
.(TIC)
Constant -2.8324"*
Target’s Utilities . Normalized = 0
Erget DemocragL(DEM7) .31 15*" -.3083
Target Drug Threat or -.0 l 60" -.0071
Problem (TDT)
Lag Target Drug Problem "' .0016 -.0252
Target Dem Level
LTDPTDL)

South/Central America .3721 ** 2.4089*"
Dummy
Target Aid % of GNI 0912"“ .0555"
(TAPGNI)
Target Trade % of GDP 0017*" 0066*"
(TTPGDP)
Target lntemal Conﬂict -.4452*" .4772
(TIC)

 

{Threat variables are lagged one period to indicate that the threat takes place in the year

prior to the outcome.

N=3019; Mean log-likelihood = -0.215656; *** z<.01, ** z<.05, * z< .1

192

 

The Importance of Regime Type

In the previous chapter on initiation, several theoretical reasons to expect
sanctions to be initiated less frequently by a democracy against another democracy were
discussed. Empirically, in cases of initiated sanctions, we found in the previous analysis
of observed sanctions that democracies were signiﬁcantly less likely to initiate sanctions
against other democracies. There are two ways that this empirical ﬁnding may come
about in cases of observed sanctions. First, fewer sanctions between democracies may
occur because democracies do not have as many disputes with other democracies. If this
were the case we should see democracies be less inclined to threaten other democracies.
Indeed this was a theoretical prediction from the formal model in chapter 4. O-HlC
predicted the United States to be less likely to threaten other democracies with sanctions.
However, the empirical evidence in this model does not support this argument. Target
democracy level (Dem7 in the ﬁrst column) is not statistically signiﬁcantly related to
threat initiation. Although the ﬁndings are at least in the predicted direction, the
substantive signiﬁcance is also minimal. Changing the target’s polity from a non-
democracy to a democracy only increases the probability that the United States will not
threaten by a very small amount (about .58% - from .9353 to .9411).

The second way that we might arrive at fewer observed cases of sanctions
between democracies is that fewer sanctions may occur because, given a threat, one or the
other side backs down before sanctions are actually implemented. Interestingly, the

results show that when the target of a threat is a democracy both the sender and the target

193

are signiﬁcantly more likely to back down before sanctions actually occur”. Thus, the
statistical results point to the conclusion that the empirical evidence of fewer sanctions
between democracies is not a result of democracies having less to ﬁght about.
Democracies are not signiﬁcantly less likely to threaten one another, which would
indicate that they are probably engaged in just as many disputes as other dyad types.
Rather, the fact that both senders and targets are more likely to back down when both
, sides are democracies points to the possibility that democracies tend to settle their
disputes prior to the imposition of sanctions. Theoretically this is to be expected. Since
democracies face higher audience costs for missteps in foreign policy they tend to back
down at signiﬁcantly higher rates when faced with a threat rather than bluff in the hope of
getting the other side to back down. However, once a democracy has stood up to the
threat, the democratic sender (the United States) is more likely to recognize the credibility
of the target’s position and back down rather than impose sanctions. This is smart
behavior because at the ﬁnal stage of the game, we see in the results that sanctions against
democracies are somewhat less likely to be successful (although insigniﬁcantly so).
While general hypothesis H2 is not supported through a statistically signiﬁcant
coefficient, the sign is at least in the hypothesized direction.

The results also tend to indicate that the biggest impact of target democracy level
is at the threat stage. After sanctions have been threatened, they are signiﬁcantly less
likely to be implemented between democracies. This can help reconcile the expectation

that sanctions are more likely to be successful against democratic governments but at the

 

’3 Substantively, however, this difference is not great. Targets are about 3% more likely to back down

when they are a democracy and senders are about .7% more likely to back down after threatening a

194

same time are empirically less likely to occur. In this case, we see empirical evidence of
the United States threatening other democracies, as they should do if they expect success,
but we also see that those cases involving democracies as targets are signiﬁcantly less
likely to ever make it to the implementation stage.

These results also provide strong support for O-H2C and O—H3C and general
hypothesis H1. As predicted, countries with a similar regime type to that of the United
States are found to be more likely to comply with the United States’ threat of sanctions.
Also, since both senders and targets are more likely to back down when the target is a
democracy, states with similar regime types are found to have a smaller probability of
sanctions given a threat.

Value of the Policy at Dispute

The value of the issue at dispute is another important element of the sender and
target’s utility functions for sanctions. Past empirical studies have concluded that larger
policy goals are harder to achieve and thus sanctions are less likely to be successful when
the demand is higher (Drezner, 1999; Dashti-Gibson et. al., 1997; Hufbauer, Schott, and
Elliot 1985; Dehejia and Wood, 1992). The results of the strategic probit analysis in table
5.5 indicate that when the United States is confronted with a target state that has a large
drug problem it is signiﬁcantly more likely to threaten sanctions. However, controlling
for other factors, we see that this threat tends to be somewhat hollow as senders are also
signiﬁcantly more likely to back down against the big drug producing/trafﬁcking
countries when they do not comply with the initial threat. Thus, we see that blufﬁng of

this nature is not without its potential for failure. Indeed, conﬁrming the ﬁndings of

 

democracy.

195

previous studies, trying to achieve a larger goal with sanctions is found to be more
difficult. Targets with larger drug problems are signiﬁcantly less likely to back down
when threatened and if sanctions are eventually imposed they are no more likely to be
successful against countries with a large drug problem than those with a small one. These
results are exactly what one would expect based on the signaling properties of the threat.
The United States drug sanctions policy is cleverly (although perhaps inappropriately
from a signaling perspective) crafted in a way that has very little cost to the United States
while having the potential to be very disruptive to the target country’s economy through
restricting access to international ﬁnancing and foreign aid. However, targets appear to
be able to see through the United States’ “cheap talk”. When the size of the drug problem
is large in targets, they are more likely to call the United States’ bluff rather than taking
the politically costly undertaking of challenging the existing drug producers and
traffickers in their country. And when this happens, the United States’ cheap talk is
revealed for what it is i.e., there is a positive and signiﬁcant likelihood that the United
States will back when a country with a large drug problem calls its bluff.

While the previous discussion centers on levels of statistical signiﬁcance, it is also
important to consider substantive levels of signiﬁcance. Substantively, the importance of
the increasing level of the target’s drug threat to the sender plays an important role.
Holding all other values in the model constant at their median level and increasing the
level of the drug threat by one standard deviation above it’s mean level produces an
approximate 39% increase in the probability of the sender threatening sanctions. Figure
5.6 shows how increasing the level of drug threat from the target affects the probability of

the sender threatening sanctions. As the amount of drugs produced and trafficked in

196

potential target countries increases there is a dramatic increase in the probability that the
United States will threaten sanctions. As noted above, however, this may be a hollow
threat and substantively we see in ﬁgure 5.7 that the sender is also more likely to back
down from that threat as the level of drug production and trafficking in the target
increases. Figure 5.7 also reveals that this propensity for the United States to back down
from its threat only has substantive importance when the target is a very large
producer/trafficker. Increasing the target’s drug production index score by one standard
deviation above the mean value only produces an increased probability of sanctions of
about .1%. But as ﬁgure 5.7 illustrates, when the drug problem index reaches levels
around 15-25, and then again between 25 and 40, the probability of the United States
backing down from its threat begins to increase dramatically.

The deductive model of the previous chapter predicted that when the sender’s
value for the policy at dispute (0' - operationalized here through drug traffic in the target)
increased it would be more likely to threaten sanctions (O-Hla). This hypothesis receives
strong support both statistically and substantively. The second stage of this hypothesis
(O-H2a) predicted that countries with a large amount of drug trafﬁc will be less likely to
comply with the United States’ threat of sanctions. This is also supported in the empirical
model by the negative coefficient on the Target Drug Threat or Problem (TDT) variable
as it relates to the target’s utility for complying with the sender’s threat. Thus, as
predicted, increasing levels of drug traffic will lead to a decreased probability of
compliance. The ﬁnal stage of this hypothesis is not supported. The theoretical model
predicts that when there is less drug traffic in the target, the probability of sanctions given

a threat will decrease. However, ﬁgure 5.7 clearly indicates that this is not the case.

197

Drugs in Democracies

An interaction term is included in the target’s utility function to see if the level of
democratic institutions and the level of drug problem are interconnected in the target’s
utility for backing down at the threat of sanctions or after sanctions are imposed. It has
been proposed that illicit narcotics trafﬁcking presents a recognizable threat to democratic
institutions (Jordon, 1999). If this were the case, I would expect democracies with a large
drug problem to welcome the added pressure from the United States to help them combat
the threat of drug traffickers corrupting their institutions. Under those conditions,
democracies with a large drug problem should be signiﬁcantly more likely to comply with
the United States at the threat level. However, there is no statistical evidence of this
happening. There is also no statistical evidence that implemented sanctions against
democracies with a larger drug problem are more likely to be successful. Democracies
with a large drug problem do not appear to be behaving any different from non-
democracies.
The cost of the sanctions

As proposed in the theoretical model, the other element that should have a
signiﬁcant impact on this interaction is the cost of sanctions. As in the other elements of
the model, it is important to consider the cost of sanctions to both the target and the
sender. Looking ﬁrst at the sender’s utility for threatening sanctions, we see that sender’s
are signiﬁcantly less likely to threaten sanctions when they are potentially costly to

themselves. This is primarily operationalized through total trade between the sender and

198

target“. While the sanctions are designed to be economically costless to the United
States, indicting a foreign government as uncooperative in the struggle against the
international trafficking of illicit narcotics certainly does not come without any cost. The
negative repercussions of this action to the United States are likely to be magniﬁed when
the accusations are leveled at important trading partners. Thus, the positive coefficient on
the total trade between sender and target is to be expected and shows that senders are less
likely to threaten sanctions when they are expected to be costly. Once again this hints at a
lack of commitment on the part of the United States with regard to combating narcotics
trafficking and helps explain why targets with signiﬁcant drug problems are seen later in
the model to be unlikely to back down in the face of the United States threat.
Furthermore, if the United States does threaten a country with which it has a large amount
of trade it is more likely to back down at the implementation stage rather than impose
sanctions if the target calls its bluff.

The substantive interpretation of the effect of the cost variables provides
additional insight into the United States’ behavior. Figure 5.8 shows how changes in the
amount of trade between the target and sender affect the sender’s propensity to threaten
sanctions, holding other variable constant at their median. Given that the mean of the
variable is 5.86, it is clear from the graph that for countries with an above average amount
of trade with the United States, threats of sanctions under this policy are highly unlikely
and changes in the amount of trade above the mean have little impact. However, for

countries that the United States has little trade with, changes in the amount of trade have

 

‘4 In this empirical model the sender is always the US so total trade between sender and target is

sufficient. If the sender varied, trade as a percentage of the sender’s GNP would be a better measure since

199

a substantively large impact. Reducing the level of trade from the mean level to one
standard deviation below the mean increases the probability of sanctions being threatened
by approximately 38%.

Next, ﬁgure 5.9 shows the probability that the United States will back down once
it has made its threat of sanctions. This ﬁgure shows that the United States is highly
unlikely to back down from its threat with countries that it conducts little to normal
amounts of trade. However, increasing trade by a standard deviation above the mean
produces an increase in the probability that the United States will back down of about
20%.

These ﬁndings are mimicked in the political realm only not nearly as strongly.
Senders are seen to be less likely (although not signiﬁcantly so) to threaten sanctions
against a democracy and signiﬁcantly more likely to back down at the implementation
stage if their bluff is called. Indicting another democratic government as complicit with
international narcotics trafficking is certain to have lasting political costs for the United
States. Thus, the more valued the relationship with the target is, the more costly
imposing this type of sanction becomes and the less likely is its use. In fact we see that
the United States is not signiﬁcantly less likely to threaten the sanctions, but given the
high political costs in terms of reputation for both the sender and target, they are
signiﬁcantly less likely to ever be implemented.

From the perspective of costs to the target, since the United States’ drug sanctions
do not speciﬁcally restrict trade with the sender it makes sense to analyze the economic

costs of sanctions from a broader perspective. Since the sanctions speciﬁcally restrict the

 

absolute measures would represent different relative costs depending on the size of the sender’s GNP.

200

target’s access to aid, targets that receive higher amounts of aid as a percent of their GNI
will face higher costs. However, not only is United States aid cut off, but the United
States also works to block the target’s access to credit from the IMF and World Bank.
This can force the target country to have to pay higher rates to secure credit ﬁom
alternative sources and restrict capital inﬂows to the target. Not only will this action be
more costly to those targets receiving high levels of aid as percent of GNI, but in general,
countries relying on high levels of trade as a percent of GDP will also face higher costs.
Less favorable terms of ﬁnancing, and restricted aid levels leading to smaller amounts of
available capital will be particularly harmful for countries relying on foreign trade.
Moreover, the political stigma of being labeled as a type of international pariah state
supporting international narcotics trafficking makes the political environment less
attractive for foreign ﬁrms considering investment and trade with that country. Thus, as
expected by the high potential cost of sanctions, countries receiving high levels of aid and
participating in high levels of trade are statistically signiﬁcantly more likely to cooperate
with the United States if threatened with sanctions (as evidenced by the positive,
signiﬁcant coefficients on these variables at the threat stage in table 5.5). Moreover, if
the sanctions are actually implemented, they are signiﬁcantly more likely to be successful
(Based on the positive, signiﬁcant coefficients on these variables at the success stage in
table 5.5).

While these variables measuring the cost of sanctions to the target have a great
deal of statistical signiﬁcance, the substantive signiﬁcance is not nearly as great as we

saw in the previous section covering the substantive signiﬁcance of the cost of sanctions

201

to the sender. Figures 5.10 thru 5.13 show the minor variation in probabilities as these
indicators are varied from minimum to maximum values.

In this section strong statistical support was found for O-Hlb and O-H2b. The
United States was seen to be signiﬁcantly less likely to threaten sanctions against
countries that it has a lot of trade with and countries that rely more on trade and aid were
found to be more likely to comply with United States’ threat of sanctions. F Luthermore,
O-H3a also receives support based on the ﬁnding that the United States will be more
likely to back down after threatening sanctions against a country with which it has high
levels of trade.
Regional Factors

Another important factor to consider is whether the United States, as the regional
hegemon, treats countries in South and Central America differently than countries in
other regions of the world. The results show clear indications of the special relationship
the United States has with other areas within its scope of inﬂuence. Holding other factors
constant, the United States is signiﬁcantly more likely to threaten sanctions against
countries in the region, and those countries are signiﬁcantly more likely to back down
when faced with the threat of sanctions. The United States is also more likely to carry out
its threat if necessary and initiate sanctions in the face of resistance by regional targets
and those targets are more likely to be forced to comply if sanctioned. This behavior can
be explained either as an example of the regional dominance of the United States or
because the threat to the United States is greater when large drug producing and
trafﬁcking states exist within its own hemisphere. Thus, states that are closer pose a

bigger threat to the United States and are more likely to draw the United States’ attention.

202

Interestingly, while the United States is generally more likely to threaten, but also more
likely to back down against states with a big drug problem, they are highly unlikely to
back down once they have threatened a country in the region. Also, generally states with
a larger drug problem are less likely to comply with the United States’ threat of sanctions,
but this is not the case with south and Central American countries. Regional countries are
signiﬁcantly more likely to back down when confronted with a threat of sanctions from
the United States.

Table 5.6 shows the substantive importance of the regional dummy variable. This
is a particularly important variable at the threat stage. Holding all other variables
constant, the United States has been much more likely over the years to threaten countries
in its region of inﬂuence. Holding other variables constant at their median levels and
changing the regional dummy from 0 to 1 produces a staggering 64.5% increase in the
probability that the United States will threaten sanctions. These regional countries are
also seen to be much more likely to cooperate with the United States demands at the
threat stage. South and Central American countries are 45.5% more likely to be deemed
as cooperating by the United States.

Stability of the Target

The ﬁnal element of the empirical model is the stability of the target government.
There is an argument in the literature that unstable countries characterized by internal
conﬂict will utilize proﬁts from illegal drug trade to ﬁnance rebellions. If this is the case,
this might present a particularly difﬁcult environment for sanctions to work whether it is
at the threat or the implementation stage. Looking at the results of the model, we see that

the United States is not signiﬁcantly more or less likely to threaten these countries.

203

However, if the threat is made, it is highly likely to reach the sanctions stage as neither
side is likely to back down. As one might expect, a target embroiled in internal conﬂict,
is less likely to be responsive to the United States threat of sanctions. The United States,
however, is less likely to be deterred from imposing sanctions against countries facing
internal conflicts once they have been identiﬁed as a major non-cooperative drug
trafficking state. Once sanctions are imposed, these states are no more or less likely to
come into compliance. Perhaps, the outcome depends on whether the government or the
opposition forces are in control of the drug resources.

The substantive effect of this variable is not great. Table 5.7 shows that the
probability of the United States making a threat against a country engaged in internal
conﬂict decreases by about 2%. Likewise, the probability of a country engaged in internal
conﬂict backing down decreases by about 2%. In further stages, this variable carries even
less substantive signiﬁcance making less than a half a percentage of difference in the

sender’s decision to back down and in the eventual success of sanctions.

204

Summary of Support for Hypotheses
Theoretical Hypotheses Not Generated from the Form_aliModel

H1: Sanctions that are threatened against democracies should be more likely to be
successful than those threatened against other regime types.

SUPPORTED

H2: Sanctions that are actually imposed against democracies should be less likely to be
successful than those imposed against other regime types.

NOT SUPPORTED BUT IN CORRECT DIRECTION

H3: In general, senders should be more successﬁrl at using economic force to coerce
targets to cooperate at the threat stage than at the implementation stage.

SUPPORTED
Stage One of Sanctions Model — Making a Threat:
O-Hl a: The United States will be more likely to threaten sanctions against countries that
have a large amount of drug traffic through their country.

SUPPORTED

O-Hlb: The United States will be less likely to threaten sanctions against countries that it
has a lot of trade with.

SUPPORTED

O-ch: The United States will be less likely to threaten sanctions against states with
similar regime types.

NOT SUPPORTED BUT IN CORRECT DIRECTION
Stage Two of Sanctions Model — Compliance with Threat:
O-H2a: Countries with a large amount of drug traffic will be less likely to comply with
the United States’ threat of sanctions.

SUPPORTED

O-H2b: Countries that trade more with the United States will be more likely to comply
with United States’ threat of sanctions.

SUPPORTED

205

O-H2c: Countries with a similar regime type to that of the United States will be more
likely to comply with the United States’ threat of sanctions.

SUPPORTED
Stage Three of Sanctions Model — Implementation of Threat:
O-H3a: When there is more trade between the sender and target, the probability of

sanctions given a threat decreases.

SUPPORTED

, O-H3b: When there is less drug trafﬁc in the target, the probability of sanctions given a
threat decreases.

NOT SUPPORTED

O-H3c: When states have similar regime types, the probability of sanctions given a threat
decreases.

SUPPORTED

206

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216

Appendix A: Development of the Dependent Variable

The following table shows, by year, the number of states that were affected by the United
States sanctions policy against countries that are not cooperating to prevent the trafﬁcking
of illicit narcotics from or through their country.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

YEAR NAMED NAMED & DECERTIFIED DECERTIFIED SAN CT ION ED
CERTIFIED & WAIVED
1987 24 19 5 2 3
1988 24 17 7 3 4
1989 24 18 7 1 6
1990 24 19 5 1 4
1991 25 20 5 1 4
1992 27 22 5 1 4
1993 27 22 5 2 3
1994 26 16 10 6 4
1995 29 18 11 6 5
1996 31 22 9 3 6
1997 31 22 9 3 6
1998 30 22 8 4 4
1999 28 22 5 4 2
2000 26 20 6 4 2
2001 24 20 4 2 2
Total 400 299 102 43 59

 

 

 

 

 

 

 

This data has been made available each year in an appendix of the International Narcotics
Control Strategy Report (IN CSR), which is prepared by the Bureau of International
Narcotics Matters for the Department of State.

217

Appendix B: Countries Affected By United States Drug Sanctions Policy Since 1987

The following is a list of all of the countries that have been affected by the policy. All
countries not listed have never been named under this policy. A complete listing of each
country’s certiﬁcation status by year can be obtained from the author upon request.

Afghanistan
Aruba
Bahamas
Belize
Bolivia
Brazil
Burma
Cambodia
China
Colombia
Dominican Republic
Ecuador
Guatemala
Haiti
Hong Kong
India

Iran

J arnaica
Laos
Lebanon
Malaysia
Mexico
Morocco
Nigeria
Pakistan
Panama
Paraguay
Peru

Syria
Taiwan
Thailand
Venezuela
Vietnam

218

Appendix C: Justification for Coding of Success Cases

Syria 1998 - removed entirely from majors list after being sanctioned the year
before.

Syria was added to the Majors List in 1987 because opium in the Syrian-
controlled Lebanese Biqa' Valley exceeded the threshold limits set by the Foreign
Assistance Act of 1961, as amended. Syria was also accused of not actively preventing
the transit of narcotics through territory under its control. Heroin and hashish produced in
Lebanon were said to be transiting Syria in small quantities bound for Europe and the
United States. Opium and morphine from Southwest Asia were also entering Syria via
Turkey and transiting to processing labs in the Syrian controlled Lebanese Biqa’ Valley.
The lack of efforts by the Syrian government to control this drug trafficking through use
of the military was a major reason for its 1987 decertiﬁcation.

However, in 1992, Syria and Lebanon launched a successful eradication
campaign, which has been sustained to the present, reducing the cultivated area of opium
to approximately 150 hectares in recent (1999) estimates. The cultivation of cannabis,
processed into hashish for primarily non-United States markets, was also reduced
drastically during the same period. Syria also assisted Lebanese ofﬁcials in implementing
a ban on further cultivation of illicit crops, participated in narcotics seizures and raids on
drug processing labs inside Lebanon, and arrested individuals dying to carry drugs
through Syrian checkpoints. Syria also cooperated with the Lebanese government to
eradicate most of the opium poppy in the Biqa' Valley. In recognition of these efforts,
Syria was removed from the Majors List in 1997. (Source: 1999 INCSR)

Iran 1999 - removed entirely from majors list after being sanctioned the year
before.

219

The Islamic Republic of Iran is a major transit route for opiates smuggled ﬁ'om
Afghanistan and Pakistan to Russia and Europe. However, according to the International
Narcotics Control Strategy Report of 1998, Iran is no longer a major drug-producing
country. A 1998 United States government survey concluded that the amount of opium
poppy cultivation was negligible, down from an estimated 3500 hectares in 1993. Even
though Iran is still a transit route, Iran's drug interdiction programs are apparently large
and aggressive, even if only partially successful at stemming the ﬂow of illicit drugs.
UNDCP teams visited Iran three times in 1998, and in January 1999 Iran and the UNDCP
signed an agreement to expand UN-Iranian cooperation, including opening a UNDCP
ofﬁce in Tehran. In the International Narcotics Control Strategy Report of 1998 it was
also reported that while regional cooperation efforts by Iran are limited in scope and
number, they have been mentioned favorably by the Dublin Group and the UNDCP, and
appear to signal a new willingness to cooperate with the international community on
counter-narcotics matters. (Source: 1998 INCSR)

Panama 1990 - Goes from sanctioned to named and certified
The United States imposed sanctions on Panama in 1988 and 1989 for lack of

cooperation with the drug effort. During this time, Panama was ruled by the authoritarian
regime of Manual Noreiga. In 1990, after the ouster of Noreiga, the United States
received full compliance from the new regime and was able to move Panama from the
decertified and sanctioned list to the named and certiﬁed as cooperating list. Later, in
1994, the United States once again decertiﬁed the Panamanian regime as not cooperating
in the effort to prevent drug production in its country and trafﬁcking to the United States.

However, unlike in 1988 and 1989 when the United States was trying to coerce the hard

220

line government of Noriega, in 1994, the United States failed to carry through with its
threat, choosing instead to grant Panama a national interest waiver. The question is, why
did the United States behave differently in 1988 and 89 when facing an authoritarian
regime than it did in 1994 when facing a democratic regime? Also, why did Panama
change its behavior and begin to cooperate with the United States in 1990 after its regime
transitioned to democracy? Based on the theory and formal model presented earlier both
the United States’ and Panama’s behavior were to be expected.

Looking at Panama’s behavior ﬁrst, several elements of their expected utility for
production and trafﬁc of illicit drugs changed between 1989 and 1990. First, the type of
regime changed from authoritarian to democratic. This alone should have changed the
incentive structure for political leaders regarding whether they should challenge or
support drug lords in their country. As democratic leaders, their incentive came ﬁ'om
successful public policy rather than providing private goods to leaders of the drug
commtmity. This coupled with a new regime not wanting to risk challenging the United
States and being discredited with failed policy could have been enough to swing
Panama’s position to one of cooperation with the United States. Another possible factor
from the model that may have inﬂuenced Panama’s decision is that as the United States’
chosen successor regime the value of cooperating with the United States and receiving
foreign aid should have increased dramatically from the expected value during the
Noriega days. In fact, the United States’ aid to Panama increased by an incredible
amount between 1989 and 1990. United States aid in 1989 was about 1 million dollars

and in 1990 it increased to approximately 400 million dollars.

221

gram

Turning to the United States’ behavior change from its use of sanctions in 1988
and 1989 to its decision to grant Panama a national interest waiver in 1994, I also see a
clear explanation provided by the theoretical model. Dealing with an autocracy in 1988
and 1989 the United States recognized the existing problem with drug production and
trafﬁc in Panama and threatened sanctions if the Panamanian government did not
cooperate with the United States in combating this problem. When Panama’s leadership
refused to comply with the United States’ demands, the United States could have backed
down and granted a national security waiver or turned up the pressure for compliance by
imposing sanctions and cutting off all aid. Since the model predicts that autocracies will
be more likely to back down after initial refusal to comply with threats, the United States’
actions made rational sense. Later, in 1994, when the United States was dealing with a
democratic government in Panama, their behavior was predictably different. Once the
leadership of Panama decided that it was in its best interest to resist the threats of the
United States, there was little chance that sanctions would lead to their backing down. At
this point, the United States turned back to its carrots of economic aid and diplomacy to
gain compliance from Panama in the following and all subsequent years. The United
States was able to show its dissatisfaction with Panama’s lack of cooperation in
combating illicit narcotics, but when Panama chose not to give in to the United States’
demands, the United States was forced to back down and grant Panama a national
security waiver. It is interesting to note that in 1994 Panama was also in a very strong
position, having just received a $7.5 million export/import bank loan to stimulate trade
with the United States. Sanctioning Panama at that point would seem counterproductive

to the United States.

222

Laos 1990 — Goes from sanctioned to named and certified
History of certiﬁcation status

Laos was one of the original countries to be decertiﬁed under the current United
States drug sanctions policy. In 1987 and 1988 Laos was decertiﬁed, but sanctions were
waived by the United States for national security reasons. In 1989 Laos ﬁnally drew
sanctions for the ﬁrst time, as the United States decertiﬁed Laos and did not issue a
waiver. However, these sanctions were short-lived, and Laos received full certiﬁcation
for cooperating in the war against international narcotics trafﬁcking in the following year,
1990. Laos maintained that status, as cooperating, for the next four years, but in 1994
they were again decertiﬁed with a national security waiver being issued. Once again,
decertiﬁcation lasted only one year and Laos returned to full certiﬁcation status in 1995
and has remained there until the present.

The Success of the United States Drug Sanctions Against Laos
In 1987 when the United States ﬁrst announced its certiﬁcation status Laos was

one of 5 countries that were decertiﬁed for lack of cooperation in the control of illicit
narcotics trafﬁc. These 5 countries were Laos, Lebanon (both receiving waivers)
Afghanistan, Iran and Syria (who were actually sanctioned). In 1988, the list was
expanded to 7 countries with Laos once again receiving a national security waiver.
Finally, in March, 1989, the United States stepped up the pressure on Laos by
decertifying it without issuing a waiver. Thus for the ﬁrst time, Laos was subjected to the
punishments under this policy.

1989 saw the United States decertify and sanction 6 countries, equaling the

highest total of any year under the policy (there were also 6 countries sanctioned in 1996

223

and 1997). There was widespread pessimism regarding the “war on drugs” at the time.
Secretary of State James Baker reported to congress at the time of the certiﬁcation
announcements that “the international war on illegal drugs was clearly not being won and
might even be slipping backwards” (Drug War May Be Slipping Backwards, Baker Says.
St. Louis Post-Dispatch. March 2, 1989).

However the prevailing thought was that the sanctions were more symbolic than
instrumental, as is evidenced by the following quote “But that step [decertiﬁcation] is
virtually meaningless, since neither nation [Burma and Laos] or the others in the same list
— Afghanistan, Iran, Syria and Panama - receives any US. aid” (Drug War May Be
Slipping Backwards, Baker Says. St. Louis Post-Dispatch. March 2, 1989). This is a
common view even today when the United States threatens sanctions against countries
with which it has minimal trade ties. However, this view is perhaps too facile since there
were many economic issues at stake and there was a growing acknowledgment by the Lao
government that it “appeared to be falling increasingly behind the dynamic economies
elsewhere in the region, including neighboring Thailand” (Gedda, 1990). And even
though it was true that Laos received very little direct aid from the United States at the
time (in 1988 and 1989 it received $1.8 and $1.5 million from the United States Food For
Peace program) it was receiving an increasing amount of aid from international
organizations. This is an important point because as part of the drug sanctions United
States representatives would be required by law to vote against Laos receiving aid from
any organizations in which it takes part. The following table shows that Laos did

potentially have a lot to lose if the United States were to use its leverage to curtail Laos’

224

access to international aid from the World Bank Group’s International Development

Association, which was there largest source of International Aid in 1989 at $53.5 million.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

International Assistance to Laos. 1986-1998
Year Total IBRD IFC IDA IDB ADB AFDB UNDP OTHUN EEC
1986 23.4 0 0 3.9 0 12 0 7.5 0 O
1987 35.9 0 0 25.8 0 0 0 6.9 3.2 0
1988 57.5 0 0 14.1 0 34.5 0 8.9 0 0
1989 94.8 0 0 53.5 0 31 O 10.3 0 0
1 990 83.7 0 0 44.7 0 39 0 0 0 0
1991 91.9 0 O 45 0 41.3 0 5.6 0 0
1992 127.7 0 O 40 0 73.6 0 8.1 6 0
1993 128.5 0 0 55 O 67 0 6.5 0 0
1994 96.5 0 O 48.4 0 43.2 0 4.9 0 0
1995 175 0 O 19.2 0 151.1 0 4.7 0 0
1996 162.4 0 0 60.7 0 91.7 0 9 1 0
1997 180.9 0 0 48 0 117.7 0 9.9 5.3 0
1 998 64 O 2. 3 34.7 0 20 0 7 O 0
Table of Abbreviations
Total Total Assistance From International Organizations
IBRD International Bank For Reconstruction and Development (World Bank
Group)

IFC International Finance Corporation (World Bank Group)

IDA International Development Association (World Bank Group)

IDB Inter-American Development Bank

ADB Asian Development Bank

AF DB African Development Bank

UNDP United Nations Development Program

OTHUN Other United Nations Programs

EEC European Economic Community

 

 

In 1990, after one year of sanctions, the United States removed Laos from the list

of countries deemed not to be cooperating in the war against drugs and provided it with

full certiﬁcation status. This is a signiﬁcant incident in the relations between Laos and

the United States and represents one of the few success stories for the United States drug

sanctions policy. This success came in two parts. First, Laos did appear to be making

strides to cut into its levels of drug production. In the two years prior to its removal from

the list, Laos went from cultivating approximately 19% of the world’s opium, to only

225

 

 

about 11%. During the year in which it was sanctioned Laos cut its cultivation of opium
from approximately 42,000 hectares to about 30,000 hectares, representing approximately
a 29% reduction in its opium cultivation in a single year. As a direct result, the opium
produced by Laos dropped from 380 MT to 275 MT (a 27% reduction in production).
Although I was not able to locate seizure data for 1989 (the year Laos was sanctioned)
1990 (the year they were removed from the list) was a record year for Laotian drug
seizures. In 1990, Laos seized more total opium products than in any other year from
1987-1999 (615Kg). Much of this total was conﬁscated in one spectacular seizure in
March of 1990 where 337.2 kilograms of reﬁned narcotics were seized by Lao border
guards in the northern province of Bokeo (Laos destroys conﬁscated narcotics. Zinhua
News Agency. June 19, 1990).

The second area of success for the policy leading to the certiﬁcation of Laos in
1990 was political. In January 1990, the United States and Laos agreed to establish
ground rules for cooperation between the two governments in combating drug trafﬁcking.
Margaret Tutwiler, speaking on behalf of the State Department announced the agreement
and reported that it marks a “signiﬁcantly improved” American relationship with Laos.
(U .S.-Laos Anit-Drug Effort. New York Times. January 10, 1990) Clearly the United
States had an integrated policy of both dangling carrots in the form of economic aid and
investment while constantly maintaining the threat of removal of all aid through drug
decertiﬁcation.

A complex set of issues was unraveling as Laos sought to shift control of its state
run economy to the private sector by selling off state enterprises in an effort to attract

foreign investment. (Gedda, George. Relationship between United States, Laos

226

Improving, Officials Say. The Associated Press. July 4, 1990). In the fall of 1989 the
United States began making overtures to Laos that would simultaneously help it reduce
drug cultivation and bring much needed capital to its economy. First, the United States
and Laos agreed on a development aid project, which was the ﬁrst anywhere in Indochina
in 15 years and promised $8.7 million to persuade farmers in northeast Laos to cultivate
crops other than opium. (Gedda, George. Relationship between United States, Laos
Improving, Ofﬁcials Say. The Associated Press. July 4, 1990). This was an essential
step in helping the Laotian government appease the portion of its population that relies
heavily on opium cultivation as their main crop. “The Laotian government must tread
warily with the hill people. Because they make up less than half the population, they have
in the past felt discriminated against by the lowland Laotians, who in effect run the
country.” (Pringle, James. “America Joins battle against opium growing in Laos.” The
Times. December 29, 1990) This group, known as the Hmong, has been cultivating
opium for well over a hundred years. Opium is their cash crop and a way of life
according to a ruling Lao People's Revolutionary party central committee member.
Aided by the $8.7 million in United States funds, "The government will try to change the
Hmong way of life by giving them more opportunity to raise cattle and eventually plant
coffee and other crops." (Pringle, James. “America Joins battle against opium growing in
Laos.” The Times. December 29, 1990)

Second, the United States began unprecedented talks to send Peace Corps
volunteers to Laos, which would mark the ﬁrst time Peace Corps volunteers would ever
work in a communist country. These talks lead to a $300,000 Peace Corps Grant to Laos

in 1991 (United States Overseas Loans and Grants). However, the agreement fell through

227

in the spring of 1992 because Laotian party leaders feared the potential subversive impact
of the Peace Corps workers. (Savada, 1995)

As is always the case in economic statecraft, the United States was not simply
giving away economic favors — it sought, and received, much in return. Along with
wanting to encourage the opening of Laos’ markets to encourage capitalism and free
trade, the United States was primarily interested in two issues: POWs and Drugs.
Pursuing the return of United States prisoners of war captured during ﬁghting in the late
60’s and early 703 continued to be an important domestic issue for United States
politicians throughout the 803 and 905. As part of this easing of tensions between the two
countries, Laos set free several thousand political prisoners and allowed United States
representatives to tour remote areas where United States warplanes crashed as part of an
increased effort to account for Americans killed in Laos during the Vietnam War.

The other major issue on the United States agenda was drug production and
trafficking. While the United States was dangling many economic incentives, the linkage
between the economic incentives and Laos’ increased effort at preventing drug smuggling
and production in its country was made clear. Thus, the January 1990 agreement “in
which Laos promised a maximum effort to halt the cultivation and trafﬁcking of opium”
(Gedda, George. Relationship between United States, Laos Improving, Officials Say.
The Associated Press. July 4, 1990) was crucial.

In April 1994, the United States again decertiﬁed Laos for its lack of cooperation
but issued a national security waiver so that sanctions were never implemented. In
essence, it was determined that the waiver was in the United States national interest in

order to exact continued cooperation on the POW/MIA issue. (Savada, 1995) This may

228

have been partly responsible for the International Development Association (IDA) only
providing $19.2 million in aid in 1995. It is interesting to note that the Asian
Development Bank (ADB) more than made up for any loss in aid from the IDA by
providing a high of $151.1 million in aid during the same year.

Coding:

Laos has continually been under threat of sanctions every year since the policy
was put in place in 1987. In 1937, 1988, 1989, and 1994, the United States has reported
that Laos was not cooperating fully in the preceding year. In 3 of those 4 years, however,
the United States failed to carry out its threat of imposing sanctions even though Laos
was deemed to be in non-compliance. In those three years a threat was made, the threat
was resisted and the threat is viewed to have failed since the sanctions were waived.

Only in 1989 was the United States willing to carry out its threat in the face of resistance
and impose the threatened sanctions. However, because in 1990 Laos responded with
clear steps to appease the United States interests, this implementation of sanctions is
coded as a success for that year. Then in the years 1990-1993 the mere threat of sanctions
is coded as having been enough to achieve cooperation. However in 1994, the threat is
again coded as having failed (with the United States backing down) since compliance was
not forthcoming and the United States was unwilling to carry out its threat. In the years
1995-2000 Laos again was seen to be taking steps to combat drug trafﬁcking and while
still under constant threat received certiﬁcation. Thus, each of these years is coded as a

successful threat.

229

 

 

 

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230

Appendix D: Information on Seizures as a measure of drug traffic.

The United States sanctions policy is described as being aimed at countries with a
high volume of illicit narcotics production or traffic. One method of measuring the
amount of illicit narcotics trafﬁcking through a country is by observing the amount of
drugs seized by that country. This is also the measure commonly used by international
institutions for measuring illegal drug trafﬁc. The UNDCP states “Data on drug seizures
provide indications of the trafﬁcking situation” (United Nations International Drug
Control Programme, 1999; p.20). Although seizure data could reﬂect both drug
trafﬁcking and the efforts of law enforcement agencies, the UNDCP maintains that
“statistics on seizures, particularly if observed over a longer period of time, reﬂect
trafﬁcking trends and facilitate the discovery of possible new developments” (United
Nations International Drug Control Programme, 1999; p.20).

This measure also has an advantage over simply measuring the amount of narcotic
drugs cultivated in a country. For example a country may not produce much of a drug on
its own, but it still may be a major transit hub for distribution of that drug throughout the
world. In this case, simply observing the amount of narcotics cultivated for that country
would lead to the conclusion that there is not a narcotics problem in that country. On the
other hand, any country that either produces or transits large quantities of illicit narcotics
would be expected to have a high amount of seizures.

Seizure data is primarily obtained from the United Nations Statistics on Narcotics
Drugs 1986-2000. The source of the UN report is statistical information furnished to the
UN by governments in accordance with international treaties on narcotics. In some cases

seizure data is not available from the UN, due to a failure by the country to report.

231

 

Whenever possible, missing data of this type is supplemented with information from the
International Narcotics Control Strategy Report (INCSR). The INCSR prepares a yearly
report to the president of the United States on Narcotics production and traffic.
Whenever possible the UN data is used because the INCSR is more likely to be biased
with respect to this analysis. Since the president basis his decision regarding the use of
sanctions on the information provided from the IN CSR, the UN data is preferred. It is
possible that the INCSR could be used to make certain countries, that the United States
wants to sanction, look like they have a larger narcotics problem. The UN is much less
likely to have this type of bias.

Seizure data is divided into three different categories depending on the type of
plant used in the manufacture of the narcotic. The three basic categories are Cannabis
(Consisting of marijuana and cannabis resin), Coca (consisting of coca leaf, coca paste,
and cocaine hcl), and Opiates derived from poppy plants (consisting of heroin, morphine
and opium).

The trafﬁcking of cannabis exceeds the trafﬁc of all other drugs in terms of
geographical distribution. Cannabis is a tobacco-like greenish or brownish material
consisting of the dried ﬂowering, fruiting tops and leaves of the cannabis plant. The
trafﬁcking of cannabis includes both herbal cannabis and cannabis resin. "Cannabis resin
means the separated resin, whether crude or puriﬁed, obtained from the cannabis plant”
(1961 Convention, art. 1 para. 1). Cannabis resin is commonly referred to as hashish,
which was once a general term for cannabis in eastern Mediterranean areas. North
America (Canada, Mexico, and USA) has accounted for about half the seizures of

cannabis during the 90’s.

232

Coca leaves are used in the production of coca paste, which is processed into
cocaine. "Coca leaf means the leaf of the coca bush from which all ecgonine, cocaine and
any other ecgonine alkaloids have been removed” (1961 Convention, art. 1, para. 1).
Throughout the 90’s the USA has produced the largest number of seizures followed by
Columbia, Mexico and Peru. It takes approximately 150 pounds of coca leaves to make
one pound of powder cocaine. The leaves of the plant are crushed into a coca paste,
which through a process of synthesis is eventually added to other substances to produce
powder cocaine. Coca paste is an intermediary product in the chemical extraction of
cocaine from coca leaves. Coca paste is an extract of the leaves of the coca bush.
Puriﬁcation of coca paste yields cocaine. Cocaine HCL is the pure white crystalline
powder that is either snorted or injected. Cocaine is the main psychoactive alkaloid
prepared from coca leaves. It can also be synthesized in a laboratory and is generally
encountered as the hydrochloride salt (hcl).

Opium and its derivatives, heroin and morphine, combine to form the third seizure
group. "Opium means the coagulated juice of the opium poppy. Opium poppy means the
plant of the species Papaver somniferum L ” (1961 Convention, art. 1, para. 1). Morphine
is an alkaloid extracted from opium or poppy straw. Heroin is a semi-synthetic opiate
synthesized from morphine. The Near and Middle East account for the majority of the

seizures of these narcotics.

233

In the fourteen year history of United States sanctions under this policy eight countries
have been sanctioned. In the table below, each of those eight countries are listed along
with the outcome of the sanctions.

 

 

 

 

 

 

 

 

 

 

Polity at
Years time of Polity at
Country Sanctioned Outcome sanctions outcome
Afghanistan 81-92 and Waived in 93 -7 -77*
95-00
Nigeria 94-98 Waived in 99 -7 4
Columbia 96-97 Waived in 98 7 7
Laos 89 Certiﬁed in 90 -7 -7
Panama 88-89 Certiﬁed in 90 -8 8
Iran 87-98 Removed -6 3
from list in 99
Syria 87-97 Removed -9 -9
from list in 98
Burma 89-00 Ongoing -8 -9

 

 

 

 

 

 

*A "-77" code for the Polity component variables indicates periods of “interregnum,”
during which there is a complete collapse of central political authority. This is most likely

to occur during periods of internal war.

234

Appendix E: US Drug Sanctions: Countries threatened and countries sanctioned

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Polity External
Years at time United Polity at
Threat of Years States time of Polity at
Country ened Threat Sanctioned sanctions Outcome sanctions outcome
Afghanis 87-00 -7 87-92 and 99 Sanctions -7 -77‘
95-00 Waived in
tan
93
Aruba 98.99 N/A
Bahamas 87-00 N/A
Belize 87-94, N/A
96-99
87-00 9 79-82
Bolivia Drug
traffic
Brazil 87-00 8 78-81, 62-
64, 77-84
Burma 87-00 -8 89-00 88-prcsent Ongoing -8 -9
Cambodi 96-00 I 92-present
a
China 92-00 -7 89-present
. 87-00 8 96-97 Sanctions 7 7
Columbr . .
a Waived in
98
Dominic 95-00 5 60—62
an
Republic
Ecuador 87-00 8 95-98
Guatema 9 l -00 3 77-present
la 93
Haiti 95-00 7 9l-94
Hong 87-00 N/A
Kong
87-00 8 65.67, 7 l ,
India 74-76, 78-
82, 98
87-98 -6 87-98 51-53, 79- Removed -6 3
Iran 81, 84- from list
present, in 99
Jamaica 87-00 10
87-00 -7 89 56-62, Certified -7 -7
Laos .
m 90
Lebanon 87-97 -77"‘ 87-97
Malaysia 87-98 5 63-66
Mexico 87-00 -3 38-47,
Morocco 87-93 -5
87-00 -7 94-98 Sanctions -7 4
Nigeria Waived in
99
87-00 -4 71, 74-76,
Pakistan 79-
present
87-00 -6 88-89 Certiﬁed -8 8
Panama .
m 90

 

 

 

 

 

 

 

 

235

 

 

Appendix E: Continued

 

 

 

 

 

 

 

 

Paraguay 87-00 -8 96
87-00 7 68, 68-74,
Peru 91-95, 95-
98,
87-97 -9 87-97 86-present Removed -9 -9
Syria from list
in 98
Taiwan 95-00 7 76-77,
Thailand 87-00 2
Venezuel 92-00 8
a
. 95-00 -7 63, 78-88,
Vietnam 54-98

 

 

 

 

 

 

 

 

236

 

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